@inproceedings{conf/www/SarwarKKR01,
author = {Sarwar, Badrul M. and Karypis, George and Konstan, Joseph A. and Riedl, John},
booktitle = {WWW},
ee = {http://doi.acm.org/10.1145/371920.372071},
interhash = {043d1aaba0f0b8c01d84edd517abedaf},
intrahash = {f349b429624935212ebeed613b89794f},
pages = {285-295},
title = {Item-based collaborative filtering recommendation algorithms.},
url = {http://www10.org/cdrom/papers/pdf/p519.pdf},
year = 2001
}
@article{10.1109/ITNG.2006.140,
address = {Los Alamitos, CA, USA},
author = {Niwa, Satoshi and Doi, Takuo and Honiden, Shinichi},
doi = {http://doi.ieeecomputersociety.org/10.1109/ITNG.2006.140},
interhash = {380f4bafa6f23f100225c700561e2e80},
intrahash = {3a7b2b52257c04b0da0702a820c3f0fc},
isbn = {0-7695-2497-4},
journal = {itng},
pages = {388-393},
publisher = {IEEE Computer Society},
title = {Web Page Recommender System based on Folksonomy Mining for ITNG 06 Submissions},
volume = 00,
year = 2006
}
@inproceedings{1458098,
address = {New York, NY, USA},
author = {Song, Yang and Zhang, Lu and Giles, C. Lee},
booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining},
doi = {http://doi.acm.org/10.1145/1458082.1458098},
interhash = {5c03bc1e658b6d44f053944418bdaec3},
intrahash = {d330a3537b4a14fbd40661424ec8e465},
isbn = {978-1-59593-991-3},
location = {Napa Valley, California, USA},
pages = {93--102},
publisher = {ACM},
title = {A sparse gaussian processes classification framework for fast tag suggestions},
url = {http://portal.acm.org/citation.cfm?id=1458098},
year = 2008
}
@inproceedings{1454017,
address = {New York, NY, USA},
author = {Symeonidis, Panagiotis and Nanopoulos, Alexandros and Manolopoulos, Yannis},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
doi = {http://doi.acm.org/10.1145/1454008.1454017},
interhash = {8ee38f4ffc05845fcb98f121fb265d48},
intrahash = {e93afe409833a632af02290bbe134cba},
isbn = {978-1-60558-093-7},
location = {Lausanne, Switzerland},
pages = {43--50},
publisher = {ACM},
title = {Tag recommendations based on tensor dimensionality reduction},
url = {http://portal.acm.org/citation.cfm?id=1454017},
year = 2008
}
@inproceedings{1454053,
address = {New York, NY, USA},
author = {Bogers, Toine and van den Bosch, Antal},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
doi = {http://doi.acm.org/10.1145/1454008.1454053},
interhash = {692eb1215676da39997ad861b681c450},
intrahash = {9d0d8ca850db6cf6177efc66e16785b7},
isbn = {978-1-60558-093-7},
location = {Lausanne, Switzerland},
pages = {287--290},
publisher = {ACM},
title = {Recommending scientific articles using citeulike},
url = {http://portal.acm.org/citation.cfm?id=1454053},
year = 2008
}
@incollection{citeulike:3149792,
abstract = {The motivation behind many Information Retrieval systems is to identify and present relevant information to people given their current goals and needs. Learning about user preferences and access patterns recent technologies make it possible to model user information needs and adapt services to meet these needs. In previous work we have presented ASSIST, a general-purpose platform which incorporates various types of social support into existing information access systems and reported on our deployment experience in a highly goal driven environment (ACM Digital Library). In this work we present our experiences in applying ASSIST to a domain where goals are less focused and where casual exploration is more dominant; YouTube. We present a general study of YouTube access patterns and detail how the ASSIST architecture affected the access patterns of users in this domain.},
author = {Coyle, Maurice and Freyne, Jill and Brusilovsky, Peter and Smyth, Barry},
citeulike-article-id = {3149792},
doi = {http://dx.doi.org/10.1007/978-3-540-70987-9\_12},
interhash = {487512d7286ca43ca9b96ee4a0efc198},
intrahash = {f75eb556b19abd7b399f2f27ae49cb1c},
journal = {Adaptive Hypermedia and Adaptive Web-Based Systems},
pages = {93--102},
posted-at = {2008-10-13 00:16:23},
priority = {2},
title = {Social Information Access for the Rest of Us: An Exploration of Social YouTube},
url = {http://www.springerlink.com/content/6h410u3w4836v866/},
year = 2008
}
@inproceedings{1454020,
address = {New York, NY, USA},
author = {Garg, Nikhil and Weber, Ingmar},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
doi = {http://doi.acm.org/10.1145/1454008.1454020},
interhash = {97a0bfae393c50cc3b59e30ce2194dbe},
intrahash = {bc41bb2e724191661fa99cb016809c37},
isbn = {978-1-60558-093-7},
location = {Lausanne, Switzerland},
pages = {67--74},
publisher = {ACM},
title = {Personalized, interactive tag recommendation for flickr},
url = {http://portal.acm.org/citation.cfm?id=1454008.1454020&coll=ACM&dl=ACM&CFID=14610234&CFTOKEN=78119200},
year = 2008
}
@article{Burke02,
author = {Burke, Robin},
file = {Burke02.pdf:Burke02.pdf:PDF},
interhash = {f40020400b8bc08adca29a987caf25d8},
intrahash = {2dd27925f83ea7b04b5fc444938b866b},
journal = {User Modeling and User Adapted Interaction},
number = 4,
owner = {stormerh},
pages = {331-370},
timestamp = {2006.11.20},
title = {Hybrid Recommender Systems, Survey and Experiments},
volume = 12,
year = 2002
}
@article{jaeschke2008tag,
abstract = {Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. },
address = {Amsterdam},
author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
doi = {10.3233/AIC-2008-0438},
editor = {Giunchiglia, Enrico},
interhash = {b2f1aba6829affc85d852ea93a8e39f7},
intrahash = {955bcf14f3272ba6eaf3dadbef6c0b10},
issn = {0921-7126},
journal = {AI Communications},
number = 4,
pages = {231-247},
publisher = {IOS Press},
title = {Tag Recommendations in Social Bookmarking Systems},
url = {http://dx.doi.org/10.3233/AIC-2008-0438},
vgwort = {63},
volume = 21,
year = 2008
}
@inproceedings{1390423,
address = {New York, NY, USA},
author = {Song, Yang and Zhuang, Ziming and Li, Huajing and Zhao, Qiankun and Li, Jia and Lee, Wang-Chien and Giles, C. Lee},
booktitle = {SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval},
doi = {http://doi.acm.org/10.1145/1390334.1390423},
interhash = {e6505664e875de06d98a6e787d4367d1},
intrahash = {525a37f6ef3d81a81686b515a148b88b},
isbn = {978-1-60558-164-4},
location = {Singapore, Singapore},
pages = {515--522},
publisher = {ACM},
title = {Real-time automatic tag recommendation},
url = {http://portal.acm.org/citation.cfm?id=1390334.1390423},
year = 2008
}
@inproceedings{Ghani02buildingrecommender,
abstract = {Online retailers have access to large amounts of transactional data but current recommender systems tend to be short-sighted in nature and usually focus on the narrow problem of pushing a set of closely related products that try to satisfy the user's current need. Most ecommerce recommender systems analyze a large amount of transactional data without actually having any idea of what the items in the transactions mean or what they say about the customers who purchased or browsed those items. In this paper, we present a case study of a system that recommends items based on a custom-built knowledge base that consists of products and associated semantic attributes. Our system fn'st extracts semantic features that characterize the domain of interest, apparel products in our case, using text learning techniques and populates a knowledge base with these products and features. The recommender system analyzes descriptions of products that the user browses or buys and automatically infers these semantic attributes to build a model of the user. This abstraction allows us to not only recommend other items in the same class of products that "match " the user model but also gives us the ability to understand the customer's "tastes " and recommend items across categories for which traditional collaborative filtering and contentbased systems are unsuitable. Our approach also allows us to "explain" the recommendations in terms of qualitative features which, we believe, enhances the user experience and helps build the user's confidence in the recommendations.},
author = {Ghani, Rayid and Fano, Andrew},
booktitle = {In 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, Malaga},
interhash = {d45155c309798ba2a0b5f27fe777b45d},
intrahash = {18c4db83e78f7d30c5774e9dca43557e},
title = {Building recommender systems using a knowledge base of product semantics},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.1272},
year = 2002
}
@inproceedings{heymann2008social,
abstract = {In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.},
address = {New York, NY, USA},
author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector},
booktitle = {SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
doi = {http://doi.acm.org/10.1145/1390334.1390425},
interhash = {bb9455c80cc9bd8cf95c951a1318dabc},
intrahash = {0e6023e192f539fe4fce9894b1fbca5a},
isbn = {978-1-60558-164-4},
location = {Singapore, Singapore},
pages = {531--538},
publisher = {ACM},
title = {Social tag prediction},
url = {http://portal.acm.org/citation.cfm?id=1390334.1390425},
year = 2008
}
@inproceedings{1502661,
abstract = {While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.},
address = {New York, NY, USA},
author = {Vig, Jesse and Sen, Shilad and Riedl, John},
booktitle = {IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces},
doi = {http://doi.acm.org/10.1145/1502650.1502661},
interhash = {a6d866cf13c75130c1969c9e40606fd1},
intrahash = {1e74fa227a24f49d8f6b17a02ea96db5},
isbn = {978-1-60558-168-2},
location = {Sanibel Island, Florida, USA},
pages = {47--56},
publisher = {ACM},
title = {Tagsplanations: explaining recommendations using tags},
url = {http://portal.acm.org/citation.cfm?id=1502650.1502661},
year = 2008
}
@inproceedings{1506255,
abstract = {In this paper we consider the problem of item recommendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or
annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The
inclusion of tags reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Experimental results on a large snapshot of the delicious bookmarking service show the scalability
of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods.},
address = {New York, NY, USA},
author = {Wetzker, Robert and Umbrath, Winfried and Said, Alan},
booktitle = {ESAIR '09: Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval},
doi = {10.1145/1506250.1506255},
interhash = {5a4e686feaa38748f7eac2c8a3afe51e},
intrahash = {c1397d5b1bf9d8305aecbb9133bdce2c},
isbn = {978-1-60558-430-0},
location = {Barcelona, Spain},
pages = {25--29},
publisher = {ACM},
title = {A hybrid approach to item recommendation in folksonomies},
url = {http://www.dai-labor.de/fileadmin/files/publications/wetzker_folksonomyrecommendation_esair2009_final.pdf},
year = 2009
}
@mastersthesis{illig2008machine,
address = {Kassel},
author = {Illig, Jens},
interhash = {65c16443f45ffd46175f68d14b4f809a},
intrahash = {9a65067da65e8301182b33b4ae292141},
school = {University of Kassel},
title = {Machine Learnability Analysis of Textclassifications in a Social Bookmarking Folksonomy},
type = {Bachelor Thesis},
year = 2008
}
@inproceedings{bollen2009suggestions,
abstract = {Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. We present experimental results that show that the power law distribution forms regardless of whether or not tag suggestions are presented to the users.},
address = {New York, NY, USA},
author = {Bollen, Dirk and Halpin, Harry},
booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
interhash = {280a97ee745f4e0409cf031a1b7ea247},
intrahash = {d7b14a0eb7fabb3cee8846802de069fe},
month = {July},
paperid = {pp161},
publisher = {ACM},
session = {Poster},
title = {The Role of Tag Suggestions in Folksonomies},
year = 2009
}
@inproceedings{mahmood2009improving,
abstract = {Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.
},
address = {New York, NY, USA},
author = {Mahmood, Tariq and Ricci, Francesco},
booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
interhash = {8b66c6c4995ed720d1b6b0029cbb36c9},
intrahash = {bb62dd3f330240a0f0adc4c12d5c5699},
month = {July},
paperid = {fp021},
publisher = {ACM},
session = {Full Paper},
title = {Improving Recommender Systems with Adaptive Conversational Strategies},
year = 2009
}
@inproceedings{1557100,
abstract = {Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.},
address = {New York, NY, USA},
author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars},
booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
doi = {http://doi.acm.org/10.1145/1557019.1557100},
interhash = {1cc85ca2ec82db2a3caf40fd1795a58a},
intrahash = {1bd672ffb8d6ba5589bb0c7deca09412},
isbn = {978-1-60558-495-9},
location = {Paris, France},
pages = {727--736},
publisher = {ACM},
title = {Learning optimal ranking with tensor factorization for tag recommendation},
url = {http://portal.acm.org/citation.cfm?id=1557019.1557100&coll=ACM&dl=ACM&type=series&idx=SERIES939&part=series&WantType=Proceedings&title=KDD},
year = 2009
}
@inproceedings{jaeschke2009testingKDML,
abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.},
author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},
booktitle = {Workshop on Knowledge Discovery, Data Mining, and Machine Learning},
editor = {Benz, Dominik and Janssen, Frederik},
interhash = {440fafda1eccf4036066f457eb6674a0},
intrahash = {5e8f40e610e723e966676772aa205f80},
month = sep,
pages = {44 --51},
title = {Testing and Evaluating Tag Recommenders in a Live System},
url = {http://lwa09.informatik.tu-darmstadt.de/pub/KDML/WebHome/kdml09_R.Jaeschke_et_al.pdf},
year = 2009
}
@incollection{citeulike:6386729,
abstract = {Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.},
address = {Berlin, Heidelberg},
author = {Liang, Huizhi and Xu, Yue and Li, Yuefeng and Nayak, Richi},
booktitle = {Rough Sets and Knowledge Technology },
chapter = 84,
citeulike-article-id = {6386729},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84},
citeulike-linkout-1 = {http://www.springerlink.com/content/f66k11352q386379},
doi = {10.1007/978-3-642-02962-2\_84},
editor = {Wen, Peng and Li, Yuefeng and Polkowski, Lech and Yao, Yiyu and Tsumoto, Shusaku and Wang, Guoyin},
interhash = {80e8a1d0263296925609dbd5b72b7d48},
intrahash = {bf98d7c1fee5f2f188f529701e70199f},
isbn = {978-3-642-02961-5},
pages = {666--673},
posted-at = {2009-12-15 15:06:20},
priority = {2},
publisher = {Springer Berlin Heidelberg},
title = {Tag Based Collaborative Filtering for Recommender Systems},
url = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84},
volume = 5589,
year = 2009
}
@inproceedings{conf/wsdm/WetzkerZBA10,
author = {Wetzker, Robert and Zimmermann, Carsten and Bauckhage, Christian and Albayrak, Sahin},
booktitle = {WSDM},
crossref = {conf/wsdm/2010},
date = {2010-02-18},
editor = {Davison, Brian D. and Suel, Torsten and Craswell, Nick and Liu, Bing},
ee = {http://doi.acm.org/10.1145/1718487.1718497},
interhash = {12e89c88182a393dae8d63287f65540d},
intrahash = {54d5f72f2993a1c60d3070782bac69ac},
isbn = {978-1-60558-889-6},
pages = {71-80},
publisher = {ACM},
title = {I tag, you tag: translating tags for advanced user models.},
url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2010.html#WetzkerZBA10},
year = 2010
}
@incollection{reference/rsh/MarinhoNSJHSS11,
author = {Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis},
booktitle = {Recommender Systems Handbook},
crossref = {reference/rsh/2011},
editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
ee = {http://dx.doi.org/10.1007/978-0-387-85820-3_19},
interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1},
intrahash = {8a520671b6ced7c4b81b1cd18274e0ee},
isbn = {978-0-387-85819-7},
pages = {615-644},
publisher = {Springer},
title = {Social Tagging Recommender Systems.},
url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#MarinhoNSJHSS11},
year = 2011
}
@article{citeulike:8506476,
abstract = {{Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.}},
address = {Los Alamitos, CA, USA},
author = {Santander, Denis P. and Brusilovsky, Peter},
citeulike-article-id = {8506476},
citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2010.261},
citeulike-linkout-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.261},
doi = {10.1109/WI-IAT.2010.261},
interhash = {dd320da969151c01cf270976c0803274},
intrahash = {2c8764f2fe11ef1ae43fc0a5b51301ae},
isbn = {978-0-7695-4191-4},
journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on},
pages = {136--142},
posted-at = {2011-01-05 00:19:36},
priority = {0},
publisher = {IEEE Computer Society},
title = {{Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}},
url = {http://dx.doi.org/10.1109/WI-IAT.2010.261},
volume = 1,
year = 2010
}
@proceedings{Gunawardana2935,
author = {Gunawardana, Asela and Shani, Guy},
interhash = {441df9b673faf85aecc45babd8883069},
intrahash = {49600df05a884106989d71dedcaa7e1b},
page = {2935−2962},
series = 2935,
title = { A Survey of Accuracy Evaluation Metrics of Recommendation Tasks },
url = {http://jmlr.csail.mit.edu/papers/v10/gunawardana09a.html},
volume = {v10},
year = 2009
}
@inproceedings{Cai:2011:LTD:1935826.1935920,
abstract = {Social tagging recommendation is an urgent and useful enabling technology for Web 2.0. In this paper, we present a systematic study of low-order tensor decomposition approach that are specifically targeted at the very sparse data problem in tagging recommendation problem. Low-order polynomials have low functional complexity, are uniquely capable of enhancing statistics and also avoids over-fitting than traditional tensor decompositions such as Tucker and Parafac decompositions. We perform extensive experiments on several datasets and compared with 6 existing methods. Experimental results demonstrate that our approach outperforms existing approaches.},
acmid = {1935920},
address = {New York, NY, USA},
author = {Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma},
booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining},
doi = {10.1145/1935826.1935920},
interhash = {414f80ad09d994af6f448446c04cd226},
intrahash = {52a9e5fd121bf7be4fa8670cc93a7197},
isbn = {978-1-4503-0493-1},
location = {Hong Kong, China},
numpages = {10},
pages = {695--704},
publisher = {ACM},
series = {WSDM '11},
title = {Low-order tensor decompositions for social tagging recommendation},
url = {http://doi.acm.org/10.1145/1935826.1935920},
year = 2011
}
@article{10.1109/CSE.2009.75,
address = {Los Alamitos, CA, USA},
author = {Zhou, Tom Chao and Ma, Hao and King, Irwin and Lyu, Michael R.},
doi = {10.1109/CSE.2009.75},
interhash = {010aefb7b22a666044909f4cea151963},
intrahash = {2b9dd91a3162d821abbe620942772464},
isbn = {978-0-7695-3823-5},
journal = {Computational Science and Engineering, IEEE International Conference on},
pages = {194-199},
publisher = {IEEE Computer Society},
title = {TagRec: Leveraging Tagging Wisdom for Recommendation},
url = {http://www.computer.org/portal/web/csdl/doi/10.1109/CSE.2009.75},
volume = 4,
year = 2009
}
@inproceedings{conf/www/SenVR09,
author = {Sen, Shilad and Vig, Jesse and Riedl, John},
booktitle = {WWW},
crossref = {conf/www/2009},
editor = {Quemada, Juan and León, Gonzalo and Maarek, Yoëlle S. and Nejdl, Wolfgang},
ee = {http://doi.acm.org/10.1145/1526709.1526800},
interhash = {4968b29a544394a5f9acd1bb8916e230},
intrahash = {8d38bdb12f6f2f89bd3c34d200e48b72},
isbn = {978-1-60558-487-4},
pages = {671-680},
publisher = {ACM},
title = {Tagommenders: connecting users to items through tags.},
url = {http://dblp.uni-trier.de/db/conf/www/www2009.html#SenVR09},
year = 2009
}
@phdthesis{bogers2009recommender,
abstract = {Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. },
address = {Tilburg, The Netherlands},
author = {Bogers, Toine},
interhash = {65b74dcabaa583a48469f3dec2ec1f62},
intrahash = {b02daac1201473600b7c8d2553865b4a},
month = dec,
school = {Tilburg University},
title = {Recommender Systems for Social Bookmarking},
url = {http://ilk.uvt.nl/~toine/phd-thesis/},
year = 2009
}
@phdthesis{jschke2011formal,
address = {[Amsterdam]},
author = {Jäschke, Robert},
interhash = {dcb2cd1cd72ae45d77c4d8755d199405},
intrahash = {1ac91a922a872523de0ce8d4984e53a3},
isbn = {9781607507079 1607507072 9783898383325 3898383326},
pages = {--},
publisher = {IOS Press},
refid = {707172013},
title = {Formal concept analysis and tag recommendations in collaborative tagging systems},
url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325},
year = 2011
}
@inproceedings{illig2009comparison,
abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. },
address = {Berlin/Heidelberg},
author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
booktitle = {Knowledge Processing and Data Analysis},
doi = {10.1007/978-3-642-22140-8_9},
editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs},
interhash = {cd3420c0f73761453320dc528b3d1e14},
intrahash = {f9d6e06ab0f2fdcebb77afa97d72e40a},
isbn = {978-3-642-22139-2},
pages = {136--149},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems},
url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9},
vgwort = {23},
volume = 6581,
year = 2011
}
@article{burke2011recommendation,
abstract = {Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.},
author = {Burke, Robin and Gemmell, Jonathan and Hotho, Andreas and Jäschke, Robert},
interhash = {3089ca25de28ef0bc80bcdebd375a6f9},
intrahash = {41dbb2c9f71440c9aa402f8966117979},
journal = {AI Magazine},
number = 3,
pages = {46--56},
publisher = {Association for the Advancement of Artificial Intelligence},
title = {Recommendation in the Social Web},
url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2373},
volume = 32,
year = 2011
}
@incollection{jaeschke2012challenges,
abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.},
address = {Berlin/Heidelberg},
affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
booktitle = {Recommender Systems for the Social Web},
doi = {10.1007/978-3-642-25694-3_3},
editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
interhash = {75b1a6f54ef54d0126d0616b5bf77563},
intrahash = {7d41d332cccc3e7ba8e7dadfb7996337},
isbn = {978-3-642-25694-3},
pages = {65--87},
publisher = {Springer},
series = {Intelligent Systems Reference Library},
title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
volume = 32,
year = 2012
}
@book{balbymarinho2012recommender,
abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
isbn = {978-1-4614-1893-1},
month = feb,
publisher = {Springer},
series = {SpringerBriefs in Electrical and Computer Engineering},
title = {Recommender Systems for Social Tagging Systems},
url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1},
year = 2012
}
@inproceedings{wetzker2010translating,
abstract = {Collaborative tagging services (folksonomies) have been among the stars of the Web 2.0 era. They allow their users to label diverse resources with freely chosen keywords (tags). Our studies of two real-world folksonomies unveil that individual users develop highly personalized vocabularies of tags. While these meet individual needs and preferences, the considerable differences between personal tag vocabularies (personomies) impede services such as social search or customized tag recommendation. In this paper, we introduce a novel user-centric tag model that allows us to derive mappings between personal tag vocabularies and the corresponding folksonomies. Using these mappings, we can infer the meaning of user-assigned tags and can predict choices of tags a user may want to assign to new items. Furthermore, our translational approach helps in reducing common problems related to tag ambiguity, synonymous tags, or multilingualism. We evaluate the applicability of our method in tag recommendation and tag-based social search. Extensive experiments show that our translational model improves the prediction accuracy in both scenarios.},
acmid = {1718497},
address = {New York, NY, USA},
author = {Wetzker, Robert and Zimmermann, Carsten and Bauckhage, Christian and Albayrak, Sahin},
booktitle = {Proceedings of the third ACM international conference on Web search and data mining},
doi = {10.1145/1718487.1718497},
interhash = {12e89c88182a393dae8d63287f65540d},
intrahash = {224e7bdc753e1823fc17828f2c760b6e},
isbn = {978-1-60558-889-6},
location = {New York, New York, USA},
numpages = {10},
pages = {71--80},
publisher = {ACM},
series = {WSDM '10},
title = {I tag, you tag: translating tags for advanced user models},
url = {http://doi.acm.org/10.1145/1718487.1718497},
year = 2010
}
@inproceedings{Freyne:2011:WRS:2043932.2044014,
abstract = {The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon. The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.
Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures
Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm},
acmid = {2044014},
address = {New York, NY, USA},
author = {Freyne, Jill and Anand, Sarabjot Singh and Guy, Ido and Hotho, Andreas},
booktitle = {Proceedings of the fifth ACM conference on Recommender systems},
doi = {10.1145/2043932.2044014},
interhash = {6171f01a8f8cd063ec257cb4809801a6},
intrahash = {11fe6dd3da33f0aa0a40c998e5193ab8},
isbn = {978-1-4503-0683-6},
location = {Chicago, Illinois, USA},
numpages = {2},
pages = {383--384},
publisher = {ACM},
series = {RecSys '11},
title = {3rd workshop on recommender systems and the social web},
url = {http://doi.acm.org/10.1145/2043932.2044014},
year = 2011
}
@misc{weston2012latent,
abstract = {Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.},
author = {Weston, Jason and Wang, Chong and Weiss, Ron and Berenzweig, Adam},
interhash = {d0ea194dd0e3a6f35c578439efcb8bff},
intrahash = {79c6771a9b032497635d5f39a39e921a},
note = {cite arxiv:1206.4603Comment: ICML2012},
title = {Latent Collaborative Retrieval},
url = {http://arxiv.org/abs/1206.4603},
year = 2012
}
@book{reference/rsh/2011,
booktitle = {Recommender Systems Handbook},
editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
ee = {http://www.springerlink.com/content/978-0-387-85819-7},
interhash = {fc9c45535069f00807f784abdd939d9f},
intrahash = {49d1b9115124c64075d6c2c559504b22},
isbn = {978-0-387-85819-7},
publisher = {Springer},
title = {Recommender Systems Handbook},
url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html},
year = 2011
}
@proceedings{Mobasher:2012:2365934,
abstract = {The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:
(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.
(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.
(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.
(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.
The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.
Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.},
address = {New York, NY, USA},
author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas},
interhash = {4a591caf39ca41da55a94a37c8c47074},
intrahash = {354947709c23c90b18dae862c46b2761},
isbn = {978-1-4503-1638-5},
location = {Dublin, Ireland},
note = 609126,
publisher = {ACM},
title = {RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
year = 2012
}
@inproceedings{Doerfel:2012:LPM:2365934.2365937,
abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.},
acmid = {2365937},
address = {New York, NY, USA},
author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
doi = {10.1145/2365934.2365937},
interhash = {beb2c81daf975eeed6e01e1b412196b1},
intrahash = {e5c2266da34a9167352615827cc4670d},
isbn = {978-1-4503-1638-5},
location = {Dublin, Ireland},
numpages = {8},
pages = {9--16},
publisher = {ACM},
series = {RSWeb '12},
title = {Leveraging publication metadata and social data into FolkRank for scientific publication recommendation},
url = {http://doi.acm.org/10.1145/2365934.2365937},
year = 2012
}
@article{Lü20121,
abstract = {The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.},
author = {Lü, Linyuan and Medo, Matúš and Yeung, Chi Ho and Zhang, Yi-Cheng and Zhang, Zi-Ke and Zhou, Tao},
doi = {10.1016/j.physrep.2012.02.006},
interhash = {408fbf13302368693d501271268cda03},
intrahash = {9594d6b87d49d22b783b9c95da1f59af},
issn = {0370-1573},
journal = {Physics Reports},
note = {Recommender Systems},
number = 1,
pages = {1 - 49},
title = {Recommender systems},
url = {http://www.sciencedirect.com/science/article/pii/S0370157312000828},
volume = 519,
year = 2012
}
@inproceedings{conf/recsys/SaidTH12,
author = {Said, Alan and Tikk, Domonkos and Hotho, Andreas},
booktitle = {RecSys},
crossref = {conf/recsys/2012},
editor = {Cunningham, Padraig and Hurley, Neil J. and Guy, Ido and Anand, Sarabjot Singh},
ee = {http://doi.acm.org/10.1145/2365952.2365959},
interhash = {ae25f45fcaa24193dee76098896cab86},
intrahash = {e030cb4883f2e989a903ed65d0a13308},
isbn = {978-1-4503-1270-7},
pages = {9-10},
publisher = {ACM},
title = {The challenge of recommender systems challenges.},
url = {http://dblp.uni-trier.de/db/conf/recsys/recsys2012.html#SaidTH12},
year = 2012
}
@inproceedings{conf/recsys/MobasherJGH12,
author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas},
booktitle = {RecSys},
crossref = {conf/recsys/2012},
editor = {Cunningham, Padraig and Hurley, Neil J. and Guy, Ido and Anand, Sarabjot Singh},
ee = {http://doi.acm.org/10.1145/2365952.2366039},
interhash = {d211f9c4cbcc5e748c848b4b55f81226},
intrahash = {fe34d757dd2ec79a40d2baa55115898d},
isbn = {978-1-4503-1270-7},
pages = {345-346},
publisher = {ACM},
title = {4th ACM RecSys workshop on recommender systems and the social web.},
url = {http://dblp.uni-trier.de/db/conf/recsys/recsys2012.html#MobasherJGH12},
year = 2012
}
@misc{mitzlaff2013recommending,
abstract = {All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine "Nameling" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.},
author = {Mitzlaff, Folke and Stumme, Gerd},
interhash = {545658b6e337858f7865b51e46d1c7a6},
intrahash = {41f92650f0f7d78366febc1832cedba9},
note = {cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013},
title = {Recommending Given Names},
url = {http://arxiv.org/abs/1302.4412},
year = 2013
}
@article{landia2013deeper,
abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
interhash = {e8095b13630452ce3ecbae582f32f4bc},
intrahash = {e585a92994be476480545eb62d741642},
journal = {cs.IR},
title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
url = {http://arxiv.org/abs/1310.1498},
volume = {1310.1498},
year = 2013
}
@inproceedings{mueller2013recommendations,
abstract = {With the rising popularity of smart mobile devices, sensor data-based
applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case.},
address = {Aachen, Germany},
author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings},
interhash = {23d1cf49208d9a0c8b883dc69d4e444d},
intrahash = {2bab3f013052bc741e795c5c61aea5c9},
issn = {1613-0073},
publisher = {CEUR-WS},
title = {Tag Recommendations for SensorFolkSonomies},
url = {http://ceur-ws.org/Vol-1066/},
volume = 1066,
year = 2013
}
@proceedings{conf/recsys/2013rsweb,
booktitle = {RSWeb@RecSys},
editor = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Hotho, Andreas and Anand, Sarabjot Singh and Guy, Ido},
ee = {http://ceur-ws.org/Vol-1066},
interhash = {31e724c09d1f4a4bbf013ecb8e1f6685},
intrahash = {aca768068f09003e97b51d48ec092ddc},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
title = {Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.},
url = {http://ceur-ws.org/Vol-1066},
volume = 1066,
year = 2013
}
@misc{kang2013lalda,
abstract = {Social media users have finite attention which limits the number of incoming
messages from friends they can process. Moreover, they pay more attention to
opinions and recommendations of some friends more than others. In this paper,
we propose LA-LDA, a latent topic model which incorporates limited,
non-uniformly divided attention in the diffusion process by which opinions and
information spread on the social network. We show that our proposed model is
able to learn more accurate user models from users' social network and item
adoption behavior than models which do not take limited attention into account.
We analyze voting on news items on the social news aggregator Digg and show
that our proposed model is better able to predict held out votes than
alternative models. Our study demonstrates that psycho-socially motivated
models have better ability to describe and predict observed behavior than
models which only consider topics.},
author = {Kang, Jeon-Hyung and Lerman, Kristina and Getoor, Lise},
interhash = {18a900ae003a2aedb3879fcaaa4e89b6},
intrahash = {84ae222ddb615ca8ae9421a29c07a8f6},
note = {cite arxiv:1301.6277Comment: The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013)},
title = {LA-LDA: A Limited Attention Topic Model for Social Recommendation},
url = {http://arxiv.org/abs/1301.6277},
year = 2013
}
@inproceedings{jannach2014sixth,
author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad},
bibsource = {dblp computer science bibliography, http://dblp.org},
booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014},
doi = {10.1145/2645710.2645786},
interhash = {b465a3695da123d6ee9de1675cb3d480},
intrahash = {5773f799bec72240eda5e6cfb6a03d7b},
pages = 395,
title = {The sixth {ACM} RecSys workshop on recommender systems and the social
web},
url = {http://doi.acm.org/10.1145/2645710.2645786},
year = 2014
}
@proceedings{jannach2014proceedings,
bibsource = {dblp computer science bibliography, http://dblp.org},
editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad},
interhash = {a1a704ec9c98e6031a1444c6eccc7c0a},
intrahash = {09cb7c63e60bd3c5e6773c9c871a8aba},
publisher = {CEUR-WS.org},
series = {{CEUR} Workshop Proceedings},
title = {Proceedings of the 6th Workshop on Recommender Systems and the Social
Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender
Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014},
url = {http://ceur-ws.org/Vol-1271},
volume = 1271,
year = 2014
}
@inproceedings{melville2002contentboosted,
abstract = {Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.},
acmid = {777124},
address = {Menlo Park, CA, USA},
author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass},
booktitle = {Eighteenth National Conference on Artificial Intelligence},
interhash = {985028099c1a29f116ad7434005895ac},
intrahash = {a4917f0299f48e403966a8003ebd50be},
isbn = {0-262-51129-0},
location = {Edmonton, Alberta, Canada},
numpages = {6},
pages = {187--192},
publisher = {American Association for Artificial Intelligence},
title = {Content-boosted Collaborative Filtering for Improved Recommendations},
url = {http://dl.acm.org/citation.cfm?id=777092.777124},
year = 2002
}