@article{naaman2008zonetag,
abstract = {We describe ZoneTag, a camera phone application that allows users to capture, annotate, and share photos directly from their phone.},
address = {Los Alamitos, CA, USA},
author = {Naaman, Mor and Nair, Rahul},
doi = {http://doi.ieeecomputersociety.org/10.1109/MMUL.2008.69},
editor = {Panchanathan, Sethuraman},
interhash = {0eaa336bc425a2710aefe9b2a4f3711d},
intrahash = {3a6067d3abc1cfdb9b59aa8e222ea664},
issn = {1070-986X},
journal = {IEEE MultiMedia},
number = 3,
pages = {34-40},
publisher = {IEEE Computer Society},
title = {ZoneTag's Collaborative Tag Suggestions: What is This Person Doing in My Phone?},
volume = 15,
year = 2008
}
@inproceedings{dkmnrt06visualizing,
author = {Dubinko, M. and Kumar, R. and Magnani, J. and Novak, J. and Raghavan, P. and Tomkins, A.},
booktitle = {Proceedings of the 15th International WWW Conference},
day = {23-25},
interhash = {b9ff2f72831a1406013a86c8202d6276},
intrahash = {dc72abb1df242c52bf2c4fa19790dcec},
month = May,
title = {Visualizing Tags over Time},
year = 2006
}
@inproceedings{xu2006tsw,
abstract = {Content organization over the Internet went through several
interesting phases of evolution: from structured directories to
unstructured Web search engines and more recently, to tagging
as a way for aggregating information, a step towards the
semantic web vision. Tagging allows ranking and data
organization to directly utilize inputs from end users, enabling
machine processing of Web content. Since tags are created by
individual users in a free form, one important problem facing
tagging is to identify most appropriate tags, while eliminating
noise and spam. For this purpose, we define a set of general
criteria for a good tagging system. These criteria include high
coverage of multiple facets to ensure good recall, least effort to
reduce the cost involved in browsing, and high popularity to
ensure tag quality. We propose a collaborative tag suggestion
algorithm using these criteria to spot high-quality tags. The
proposed algorithm employs a goodness measure for tags derived
from collective user authorities to combat spam. The goodness
measure is iteratively adjusted by a reward-penalty algorithm,
which also incorporates other sources of tags, e.g., content-based
auto-generated tags. Our experiments based on My Web 2.0 show
that the algorithm is effective.},
address = {Edinburgh, Scotland},
author = {Xu, Z. and Fu, Y. and Mao, J. and Su, D.},
booktitle = {Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006},
interhash = {e18fd92b0ffa21b9f0cbb3a2fe15b873},
intrahash = {7e367bbd3d0fe37ab2dd5d9191c4eadd},
month = May,
title = {Towards the semantic web: Collaborative tag suggestions},
url = {http://www.ibiblio.org/www_tagging/2006/13.pdf},
year = 2006
}
@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{veres2006language,
abstract = {Folksonomies are classification schemes that emerge from the collective actions of users who tag resources with an unrestricted
set of key terms. There has been a flurry of activity in this domain recently with a number of high profile web sites andsearch engines adopting the practice. They have sparked a great deal of excitement and debate in the popular and technicalliterature, accompanied by a number of analyses of the statistical properties of tagging behavior. However, none has addressedthe deep nature of folksonomies. What is the nature of a tag? Where does it come from? How is it related to a resource? Inthis paper we present a study in which the linguistic properties of folksonomies reveal them to contain, on the one hand,tags that are similar to standard categories in taxonomies. But on the other hand, they contain additional tags to describeclass properties. The implications of the findings for the relationship between folksonomy and ontology are discussed.},
address = {Berlin/Heidelberg},
author = {Veres, Csaba},
booktitle = {Natural Language Processing and Information Systems},
doi = {10.1007/11765448},
editor = {Kop, Christian and Fliedl, Günther and Mayr, Heinrich C. and Métais, Elisabeth},
interhash = {1787dec43f3c11153fc9d2617af8829c},
intrahash = {d0e5be1774a6094049df3e6d604f1957},
isbn = {978-3-540-34616-6},
issn = {0302-9743},
pages = {58--69},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {The Language of Folksonomies: What Tags Reveal About User Classification},
url = {http://dx.doi.org/10.1007/11765448_6},
volume = 3999,
year = 2006
}
@inproceedings{firan2007benefit,
abstract = {Collaborative tagging, i.e. the process of assigning metadata in the form of keywords to shared content by many users, has emerged as an important way to provide information about resources on the Web and elsewhere. Such keywords (tags) are used to enable the organization of information within personal information spaces, such as photo collections, but can also be shared, allowing browsing and searching with the help of tags attached by other users to information resources from the Web. Recent research has shown that such tag distributions stabilize over time and can be used to improve search on the Web. In this paper we are interested in another aspect, namely how they characterize the user and enable personalized recommendations. Using data from a frequently used music search portal, Last.fm, we analyze tag usage and statistics and investigate the use of tag-based user profiles in contrast to conventional user profiles based on song and track usage. We specify recommendation algorithms based on tag user profiles, and explore how collaborative filtering recommendations based on these tag profiles are different from recommendations based on song/track profiles. Finally, we describe a new search-based method, which uses tags to recommend songs interesting to a user, yielding substantially improved results. The paper finishes with a discussion of some future work to further improve tag-based search and recommendation in community web sites.},
address = {Washington, DC, USA},
author = {Firan, Claudiu S. and Nejdl, Wolfgang and Paiu, Raluca},
booktitle = {LA-WEB '07: Proceedings of the 2007 Latin American Web Conference},
doi = {http://dx.doi.org/10.1109/LA-WEB.2007.24},
interhash = {3261a775322d01ef56865452946a33f6},
intrahash = {23a6d3aac77330ca699f6d7464b5abb4},
isbn = {0-7695-3008-7},
pages = {32--41},
publisher = {IEEE Computer Society},
title = {The Benefit of Using Tag-Based Profiles},
url = {http://portal.acm.org/citation.cfm?id=1317537.1318430&jmp=abstract&coll=GUIDE&dl=GUIDE&CFID=66852723&CFTOKEN=79035243#abstract},
year = 2007
}
@inproceedings{sen2006tagging,
abstract = {A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.},
address = {New York, NY, USA},
author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al Mamunur and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, F. Maxwell and Riedl, John},
booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work},
doi = {http://doi.acm.org/10.1145/1180875.1180904},
interhash = {96b20bffcbc91e528461529935524b90},
intrahash = {582641c05e7a0b9396945a951822c83f},
isbn = {1-59593-249-6},
location = {Banff, Alberta, Canada},
pages = {181--190},
publisher = {ACM},
title = {tagging, communities, vocabulary, evolution},
url = {http://portal.acm.org/citation.cfm?id=1180904},
year = 2006
}
@inproceedings{sood2007tagassist,
abstract = {In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion.
Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.},
author = {Sood, Sanjay and Owsley, Sara and Hammond, Kristian and Birnbaum, Larry},
booktitle = {Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007)},
interhash = {1a946aea1d46f79ad85c25add991bef6},
intrahash = {bb8e778a61c315ad946dfec71b13cc8b},
title = {{TagAssist: Automatic Tag Suggestion for Blog Posts}},
url = {http://icwsm.org/papers/2--Sood-Owsley-Hammond-Birnbaum.pdf},
year = 2007
}
@article{kaser2007tagcloud,
abstract = {Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that con- sist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well. },
author = {Kaser, Owen and Lemire, Daniel},
date = {2008-01-02},
interhash = {cb6ed5e3340cf684ec55299adc65e1a9},
intrahash = {56270d1311c066a3852bea23eeb8d484},
journal = {CoRR},
note = {informal publication},
title = {Tag-Cloud Drawing: Algorithms for Cloud Visualization},
url = {http://arxiv.org/abs/cs/0703109},
volume = {abs/cs/0703109},
year = 2007
}
@article{zhang2011tagaware,
abstract = {In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.},
affiliation = {Institute of Information Economy, Hangzhou Normal University, Hangzhou, 310036 China},
author = {Zhang, Zi-Ke and Zhou, Tao and Zhang, Yi-Cheng},
doi = {10.1007/s11390-011-0176-1},
interhash = {c1f382191eab1f80aaf8cf425c376600},
intrahash = {67b105a941f0a557c6d457447625cbfb},
issn = {1000-9000},
issue = {5},
journal = {Journal of Computer Science and Technology},
keyword = {Computer Science},
number = 5,
pages = {767--777},
publisher = {Springer Boston},
title = {Tag-Aware Recommender Systems: A State-of-the-Art Survey},
url = {http://dx.doi.org/10.1007/s11390-011-0176-1},
volume = 26,
year = 2011
}
@inproceedings{tso2008tag,
abstract = {Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.},
address = {New York, NY, USA},
author = {Tso-Sutter, Karen H. L. and Marinho, Leandro Balby and Schmidt-Thieme, Lars},
booktitle = {SAC '08: Proceedings of the 2008 ACM symposium on Applied computing},
doi = {http://doi.acm.org/10.1145/1363686.1364171},
interhash = {61f74fe4bb3a72220c69438010ae9962},
intrahash = {792034671682f8720177801e2729d4c7},
isbn = {978-1-59593-753-7},
location = {Fortaleza, Ceara, Brazil},
pages = {1995--1999},
publisher = {ACM},
title = {Tag-aware recommender systems by fusion of collaborative filtering algorithms},
url = {http://portal.acm.org/citation.cfm?id=1364171},
year = 2008
}
@inproceedings{lipzcak2009tag,
author = {Lipczak, Marek and Hu, Yeming and Kollet, Yael and Milios, Evangelos},
booktitle = {ECML PKDD Discovery Challenge 2009 (DC09)},
crossref = {eisterlehner2009ecmlpkdd},
editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
interhash = {042a9e208f55e00172e2100dc7f356d5},
intrahash = {dc2bfb649e4b0ffe2da37e9e25e0404e},
issn = {1613-0073},
month = sep,
pages = {157--172},
series = {CEUR-WS.org},
title = {Tag Sources for Recommendation in Collaborative Tagging Systems},
url = {http://ceur-ws.org/Vol-497/paper_19.pdf},
volume = 497,
year = 2009
}
@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},
month = dec,
number = 4,
pages = {231--247},
publisher = {IOS Press},
title = {Tag Recommendations in Social Bookmarking Systems},
url = {http://www.kde.cs.uni-kassel.de/pub/pdf/jaeschke2008tag.pdf},
vgwort = {63},
volume = 21,
year = 2008
}
@inproceedings{suchanek2008social,
abstract = {This paper aims to quantify two common assumptions about social tagging: (1) that tags are "meaningful" and (2) that the tagging process is influenced by tag suggestions. For (1), we analyze the semantic properties of tags and the relationship between the tags and the content of the tagged page. Our analysis is based on a corpus of search keywords, contents, titles, and tags applied to several thousand popular Web pages. Among other results, we find that the more popular tags of a page tend to be the more meaningful ones. For (2), we develop a model of how the influence of tag suggestions can be measured. From a user study with over 4,000 participants, we conclude that roughly one third of the tag applications may be induced by the suggestions. Our results would be of interest for designers of social tagging systems and are a step towards understanding how to best leverage social tags for applications such as search and information extraction.},
address = {New York, NY, USA},
author = {Suchanek, Fabian M. and Vojnovic, Milan and Gunawardena, Dinan},
booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management},
doi = {http://doi.acm.org/10.1145/1458082.1458114},
interhash = {1bca5a66a6a562258e0c0357545fed34},
intrahash = {732270af27da1046616415c8335382f6},
isbn = {978-1-59593-991-3},
location = {Napa Valley, California, USA},
pages = {223--232},
publisher = {ACM},
title = {Social tags: meaning and suggestions},
url = {http://portal.acm.org/citation.cfm?id=1458114},
year = 2008
}
@inproceedings{cao2009social,
author = {Cao, Hao and Xie, Maoqiang and Xue, Lian and Liu, Chunhua and Teng, Fei and Huang, Yalou},
crossref = {eisterlehner2009ecmlpkdd},
editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
interhash = {0fe41be4d701afb127ad60cbda517467},
intrahash = {ca6cf1ef17ca098cdd6015e3ca1e4f7c},
issn = {1613-0073},
month = {September},
pages = {35--48},
series = {CEUR-WS.org},
title = {Social Tag Prediction Base on Supervised Ranking Model},
volume = 497,
year = 2009
}
@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{zanardi2008social,
abstract = {Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0. We measure users' similarity based on their past tag activity. We infer tags' relationships based on their association to content. We then propose a mechanism to answer a user's query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. A thorough evaluation conducted on the CiteULike dataset demonstrates that Social Ranking neatly improves coverage, while not compromising on accuracy.},
address = {New York, NY, USA},
author = {Zanardi, Valentina and Capra, Licia},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
doi = {http://doi.acm.org/10.1145/1454008.1454018},
interhash = {dcf815f49a37bf32408fd66ae77d85c3},
intrahash = {e9e606a98ce7f2fed11c339a500a2f88},
isbn = {978-1-60558-093-7},
location = {Lausanne, Switzerland},
pages = {51--58},
publisher = {ACM},
title = {Social ranking: uncovering relevant content using tag-based recommender systems},
url = {http://portal.acm.org/citation.cfm?id=1454008.1454018},
year = 2008
}
@inproceedings{cattuto2008semantic,
abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.},
address = {Patras, Greece},
author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
booktitle = {Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)},
interhash = {cc62b733f6e0402db966d6dbf1b7711f},
intrahash = {3b0aca61b24e4343bd80390614e3066e},
isbn = {978-960-89282-6-8},
month = jul,
pages = {39--43},
title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems},
url = {http://olp.dfki.de/olp3/},
year = 2008
}
@inproceedings{Basile2007recommending,
abstract = {Collaborative tagging systems are harnessing the power of online
communities, making the task of knowledge contribution more attractive to a
broader audience of Web users. In particular, social bookmarking systems have
shifted the organization of bookmarks from an individual activity performed on
a personal desktop to a collective endeavor over the Web. In such a context,
suggestive tagging has proved to be helpful in consolidating the usage of tags,
leading to a quick convergence to a folksonomy.
In a social bookmarking system, users' annotations can be regarded as a reliable
indicator of interests and preferences. A recommender system is able to learn
user interests and preferences during the interaction in order to construct a user
profile.
In this paper, we propose a smart tag recommender able to learn from past user
interaction as well as the content of the resources to annotate. The aim of the
system is to support users of current social bookmarking systems by providing a
list of new meaningful tags. The proposed system is based on ITem
Recommender, a content-based recommender previously used in a Digital
Library scenario.},
author = {Basile, Pierpaolo and Gendarmi, Domenico and Lanubile, Filippo and Semeraro, Giovanni},
booktitle = {Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007)},
interhash = {8eb91925848064174cb8535812943908},
intrahash = {24054e246e7e1718824cf18e0c46f680},
pages = {22-29},
title = {Recommending Smart Tags in a Social Bookmarking System},
url = {http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/RecommendingSmartTags.pdf},
year = 2007
}
@techreport{Vojnovic2007ranking,
abstract = {We consider collaborative tagging systems where users can attach tags to information objects. Such systems are widely used to add keywords meta-data to photos, videos, or web pages (social bookmarking applications). The meta-data is then used by information retrieval mechanism to provide accurate query answers. To that end, the goal of collaborative tagging systems is to quickly discover the true ranking of a tag for an information object with respect to a given ranking criteria. In this paper, we consider the popularity rank as a ranking criteria. Many collaborative tagging systems help users tagging of an object by making suggestions based on the tagging history of an information object. The problem with
making tag suggestions is that they may reinforce some tags and the system fails to discover the true popularity rank of a tag for a given information object. To investigate this issue, we propose and study several algorithms for ranking and suggesting tags in collaborative tagging systems where we focus on the following design objectives: (a) learn true popularity rank, (b) make relevant suggestions, and (c) learn fast. We find that simple incremental updates of the suggestion set, which besides suggestion set size require no
configuration constants, offer good performance. Performance is evaluated by analysis and numerical results for which we used a dataset of complete tagging histories of urls that we crawled from a popular social bookmarking web service over a month period.},
author = {Vojnovic, M. and Cruise, J. and Gunawardena, D. and Marbach, P.},
date = {Feb 2007},
howpublished = {MSR-TR-2007-06},
institution = {Microsoft Research},
interhash = {687129a4106fab9a2cd1c032ae52fc73},
intrahash = {b2ca76b60ffbd28b55f3707085986e7a},
number = {MSR-TR-2007-06},
tech = {TR-2007-06},
title = {Ranking and Suggesting Tags in Collaborative Tagging Applications},
url = {http://research.microsoft.com/~milanv/MSR-TR-2007-06.pdf},
year = 2007
}
@inproceedings{garg2008personalized,
abstract = {We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust.
Apart from this new algorithm and its detailed analysis, our main contributions are (i) a clean methodology which leads to conservative performance estimates, (ii) showing how classical classification algorithms can be applied to this problem, (iii) introducing a new cost measure, which captures the effort of the whole tagging process, (iv) clearly identifying, when purely local schemes (using only a user's tagging history) can or cannot be improved by global schemes (using everybody's tagging history).},
address = {New York, NY, USA},
author = {Garg, Nikhil and Weber, Ingmar},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
doi = {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=1454020},
year = 2008
}
@inproceedings{1367657,
abstract = {We present a system for personalized tag suggestion for Flickr: While the user is entering/selecting new tags for a particular picture, the system is suggesting related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe three algorithms which can be applied to this problem. In experiments, our best-performing method yields an improvement in precision of 10-15% over a baseline method very similar to the system currently used by Flickr. Our system is accessible at http://ltaa5.epfl.ch/flickr-tags/.
To the best of our knowledge, this is the first study on tag suggestion in a setting where (i) no full text information is available, such as for blogs, (ii) no item has been tagged by more than one person, such as for social bookmarking sites, and (iii) suggestions are dynamically updated, requiring efficient yet effective algorithms.},
address = {New York, NY, USA},
author = {Garg, Nikhil and Weber, Ingmar},
booktitle = {WWW '08: Proceeding of the 17th International Conference on World Wide Web},
doi = {http://doi.acm.org/10.1145/1367497.1367657},
interhash = {e967f2241dc588de1397ad2ee337e8f0},
intrahash = {de7a51056aadee86c12d3ee59b674f81},
isbn = {978-1-60558-085-2},
location = {Beijing, China},
pages = {1063--1064},
publisher = {ACM},
title = {Personalized tag suggestion for flickr},
url = {http://portal.acm.org/citation.cfm?id=1367657},
year = 2008
}
@inproceedings{byde2007personalized,
abstract = {This short paper describes a novel technique for generating personalized
tag recommendations for users of social book- marking sites such
as del.icio.us. Existing techniques recom- mend tags on the basis
of their popularity among the group of all users; on the basis of
recent use; or on the basis of simple heuristics to extract keywords
from the url being tagged. Our method is designed to complement these
approaches, and is based on recommending tags from urls that are
similar to the one in question, according to two distinct similarity
metrics, whose principal utility covers complementary cases.},
author = {Byde, Andrew and Wan, Hui and Cayzer, Steve},
booktitle = {Proceedings of the International Conference on Weblogs and Social
Media},
interhash = {38aaca7e5b9c508a5901f4109dabaa69},
intrahash = {157846898c1c2a65c265a913ebac115a},
month = {March},
title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics},
url = {http://www.icwsm.org/papers/paper47.html},
year = 2007
}
@inproceedings{shepitsen2008personalized,
abstract = {Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.},
address = {New York, NY, USA},
author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
doi = {10.1145/1454008.1454048},
interhash = {c9028129dd7cd8314673bd64cbb6198e},
intrahash = {a7552f8d8d5db4f867ae6e94e1a4442f},
isbn = {978-1-60558-093-7},
location = {Lausanne, Switzerland},
pages = {259--266},
publisher = {ACM},
title = {Personalized recommendation in social tagging systems using hierarchical clustering},
url = {http://portal.acm.org/citation.cfm?id=1454008.1454048},
year = 2008
}
@inproceedings{rendle2010pairwise,
abstract = {Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.},
acmid = {1718498},
address = {New York, NY, USA},
author = {Rendle, Steffen and Schmidt-Thieme, Lars},
booktitle = {Proceedings of the third ACM international conference on Web search and data mining},
doi = {10.1145/1718487.1718498},
interhash = {ce8fbdf2afb954579cdb58104fb683a7},
intrahash = {10fe730b391b08031f3103f9cdbb6e1a},
isbn = {978-1-60558-889-6},
location = {New York, New York, USA},
numpages = {10},
pages = {81--90},
publisher = {ACM},
title = {Pairwise interaction tensor factorization for personalized tag recommendation},
url = {http://doi.acm.org/10.1145/1718487.1718498},
year = 2010
}
@inproceedings{chirita2008ptag,
abstract = {The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web page, P-TAG produces keywords relevant both to its textual content, but also to the data residing on the surfer's Desktop, thus expressing a personalized viewpoint. Empirical evaluations with several algorithms pursuing this approach showed very promising results. We are therefore very confident that such a user oriented automatic tagging approach can provide large scale personalized metadata annotations as an important step towards realizing the Semantic Web.},
address = {New York, NY, USA},
author = {Chirita, Paul Alexandru and Costache, Stefania and Nejdl, Wolfgang and Handschuh, Siegfried},
booktitle = {WWW '07: Proceedings of the 16th International Conference on World Wide Web},
doi = {http://doi.acm.org/10.1145/1242572.1242686},
interhash = {93612f3a17257a5d0d73e95d59d0c408},
intrahash = {e4656fefb25cbbff07cfba226601a6e7},
isbn = {978-1-59593-654-7},
location = {Banff, Alberta, Canada},
pages = {845--854},
publisher = {ACM},
title = {P-TAG: large scale automatic generation of personalized annotation tags for the web},
url = {http://portal.acm.org/citation.cfm?id=1242686},
year = 2007
}
@inproceedings{rendle2009learning,
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 Balby Marinho, Leandro 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 = {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?doid=1557019.1557100},
year = 2009
}
@inproceedings{sarwar2001item,
abstract = {Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Item-based techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different item-based recommendation generation algorithms. We look into different techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and different techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available user-based algorithms.},
address = {New York, NY, USA},
author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John},
booktitle = {WWW '01: Proceedings of the 10th International Conference on World Wide Web},
doi = {10.1145/371920.372071},
interhash = {043d1aaba0f0b8c01d84edd517abedaf},
intrahash = {a6461157c8102d34b8001c7d33a42684},
isbn = {1-58113-348-0},
location = {Hong Kong},
pages = {285--295},
publisher = {ACM},
title = {Item-based collaborative filtering recommendation algorithms},
url = {http://portal.acm.org/citation.cfm?id=372071},
year = 2001
}
@inproceedings{hassanmontero2006improving,
address = {Merida, Spain},
author = {Hassan-Montero, Y. and Herrero-Solana, V.},
booktitle = {Proc. InSciT 2006},
day = {25--28},
interhash = {4458142370e3c6a4fe656af2f822a0dc},
intrahash = {99ffb0c3a76afe508f5ff6b219f72515},
month = Oct,
title = {{Improving Tag-Clouds as Visual Information Retrieval Interfaces}},
year = 2006
}
@inproceedings{rae2010improving,
abstract = {In this paper we address the task of recommending additional tags to partially annotated media objects, in our case images. We propose an extendable framework that can recommend tags using a combination of different personalised and collective contexts. We combine information from four contexts: (1) all the photos in the system, (2) a user's own photos, (3) the photos of a user's social contacts, and (4) the photos posted in the groups of which a user is a member. Variants of methods (1) and (2) have been proposed in previous work, but the use of (3) and (4) is novel.
For each of the contexts we use the same probabilistic model and Borda Count based aggregation approach to generate recommendations from different contexts into a unified ranking of recommended tags. We evaluate our system using a large set of real-world data from Flickr. We show that by using personalised contexts we can significantly improve tag recommendation compared to using collective knowledge alone. We also analyse our experimental results to explore the capabilities of our system with respect to a user's social behaviour.},
address = {Paris, France},
author = {Rae, Adam and Sigurbjörnsson, Börkur and van Zwol, Roelof},
booktitle = {Adaptivity, Personalization and Fusion of Heterogeneous Information},
interhash = {2595ff47e852a64c7f1c88b915c7e9ad},
intrahash = {98034c615577fd3558fd326fbe03f894},
location = {Paris, France},
pages = {92--99},
publisher = {Le Centre De Hautes Etudes Internationales d'Informatique Documentaire},
series = {RIAO '10},
title = {Improving tag recommendation using social networks},
url = {http://portal.acm.org/citation.cfm?id=1937055.1937077},
year = 2010
}
@book{jaeschke2011formal,
abstract = {One of the most noticeable innovation that emerged with the advent of the Web 2.0 and the focal point of this thesis are collaborative tagging systems. They allow users to annotate arbitrary resources with freely chosen keywords, so called tags. The tags are used for navigation, finding resources, and serendipitous browsing and thus provide an immediate benefit for the user. By now, several systems for tagging photos, web links, publication references, videos, etc. have attracted millions of users which in turn annotated countless resources. Tagging gained so much popularity that it spread into other applications like web browsers, software packet managers, and even file systems. Therefore, the relevance of the methods presented in this thesis goes beyond the Web 2.0. The conceptual structure underlying collaborative tagging systems is called folksonomy. It can be represented as a tripartite hypergraph with user, tag, and resource nodes. Each edge of the graph expresses the fact that a user annotated a resource with a tag. This social network constitutes a lightweight conceptual structure that is not formalized, but rather implicit and thus needs to be extracted with knowledge discovery methods. In this thesis a new data mining task – the mining of all frequent tri-concepts – is presented, together with an efficient algorithm for discovering such implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. Extending the theory of triadic Formal Concept Analysis, we provide a formal definition of the problem, and present an efficient algorithm for its solution. We show the applicability of our approach on three large real-world examples and thereby perform a conceptual clustering of two collaborative tagging systems. Finally, we introduce neighborhoods of triadic concepts as basis for a lightweight visualization of tri-lattices. The social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind, has been developed by our research group. Besides being a useful tool for many scientists, it provides interested researchers a basis for the evaluation and integration of their knowledge discovery methods. This thesis introduces BibSonomy as an exemplary collaborative tagging system and gives an overview of its architecture and some of its features. Furthermore, BibSonomy is used as foundation for evaluating and integrating some of the discussed approaches. Collaborative tagging 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 this thesis we evaluate and compare several recommendation algorithms on large-scale real-world 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 co-occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag co-occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We demonstrate how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. Furthermore, we show how to integrate recommendation methods into a real tagging system, record and evaluate their performance by describing the tag recommendation framework we developed for 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. We also present an evaluation of the framework which demonstrates its power. The folksonomy graph shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Clicklogs of web search engines can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large folksonomy snapshot and on query logs of two large search engines. We find that all of the three datasets exhibit similar structural properties and thus conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of collaborative tagging users is driven by similar dynamics. In this thesis we further transfer the folksonomy paradigm to the Social Semantic Desktop – a new model of computer desktop that uses Semantic Web technologies to better link information items. There we apply community support methods to the folksonomy found in the network of social semantic desktops. Thus, we connect knowledge discovery for folksonomies with semantic technologies. Alltogether, the research in this thesis is centered around collaborative tagging systems and their underlying datastructure – folksonomies – and thereby paves the way for the further dissemination of this successful knowledge management paradigm. },
address = {Heidelberg, Germany},
author = {Jäschke, Robert},
interhash = {dcb2cd1cd72ae45d77c4d8755d199405},
intrahash = {9db90c2ff04f514ada9f6b50fde46065},
isbn = {978-3-89838-332-5},
month = jan,
publisher = {Akademische Verlagsgesellschaft AKA},
series = {Dissertationen zur Künstlichen Intelligenz},
title = {Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems},
url = {http://www.aka-verlag.com/de/detail?ean=978-3-89838-332-5},
vgwort = {413},
volume = 332,
year = 2011
}
@article{gt06folksonomies,
author = {Guy, Marieke and Tonkin, Emma},
doi = {10.1045/january2006-guy},
interhash = {535e0aea1bcbd7feb85a7495f284a589},
intrahash = {a62decf2da83f2d9e10ff7846296699b},
journal = {D-Lib Magazine},
month = {January},
note = {ISSN 1082-9873},
number = 1,
title = {Folksonomies - Tidying up Tags?},
url = {http://dlib.org/dlib/january06/guy/01guy.html},
volume = 12,
year = 2006
}
@inproceedings{sigurbjoernsson2008flickr,
address = {New York, NY, USA},
author = {Sigurbjörnsson, Börkur and van Zwol, Roelof},
booktitle = {WWW '08: Proceeding of the 17th International Conference on World Wide Web},
doi = {http://doi.acm.org/10.1145/1367497.1367542},
interhash = {7baae91cfc1321c30d4dcc94a47af15f},
intrahash = {5fb3e6b2e645fb2e5871223cbe18db52},
isbn = {978-1-60558-085-2},
location = {Beijing, China},
pages = {327--336},
publisher = {ACM},
title = {Flickr tag recommendation based on collective knowledge},
url = {http://portal.acm.org/citation.cfm?id=1367497.1367542},
year = 2008
}
@misc{illig2006entwurf,
author = {Illig, Jens},
howpublished = {Project report},
institution = {Fachgebiet Wissensverarbeitung, Universität Kassel},
interhash = {cb0aab1eb647c26b0b26b0edf74dd24a},
intrahash = {7c95058aa4c600d11c80319a07e94878},
title = {Entwurf und Integration eines Item-Based Collaborative Filtering
Tag Recommender Systems in das BibSonomy-Projekt},
url = {http://www.kde.cs.uni-kassel.de/lehre/arbeiten/documents/illig2006entwurf.pdf},
year = 2006
}
@inproceedings{yanfei2006cubic,
abstract = {Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different usersâ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.
ER -},
author = {Xu, Yanfei and Zhang, Liang and Liu, Wei},
booktitle = {APWeb},
editor = {Zhou, Xiaofang and Li, Jianzhong and Shen, Heng Tao and Kitsuregawa, Masaru and Zhang, Yanchun},
ee = {http://dx.doi.org/10.1007/11610113_66},
interhash = {edf999afa5a0ff81e53b0c859b466659},
intrahash = {98dd99b5f4189c8427163fd5a7568e1d},
isbn = {3-540-31142-4},
journal = {Frontiers of WWW Research and Development - APWeb 2006},
pages = {733--738},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
url = {http://dx.doi.org/10.1007/11610113_66},
volume = 3841,
year = 2006
}
@inproceedings{xi2009content,
author = {Si, Xiance and Liu, Zhiyuan and Li, Peng and Jiang, Qixia and Sun, Maosong},
crossref = {eisterlehner2009ecmlpkdd},
editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
interhash = {060d0b9532600a70bccbabd8628f64a9},
intrahash = {de2233e0713a1cefbf5f5ccde074e31d},
issn = {1613-0073},
month = {September},
pages = {243--260},
series = {CEUR-WS.org},
title = {Content-based and Graph-based Tag Suggestion},
volume = 497,
year = 2009
}
@inproceedings{adrian2007contag,
abstract = {ConTag is an approach to generate semantic tag recommendations for documents
based on Semantic Web ontologies and Web 2.0 services. We designed and implemented a process to normalize documents to RDF format, extract document topics using Web 2.0 services and finally match extracted topics to a Semantic
web ontology.
Due to ConTag we are able to show that the information provided by Web 2.0 services in combination with a Semantic Web ontology enables the generation of relevant semantic tag recommendations for documents. The main contribution of this work is a semantic tag recommendation process based on a choreography of Web
2.0 services.},
author = {Adrian, Benjamin and Sauermann, Leo and Roth-Berghofer, Thomas},
booktitle = {Proceedings of I-Semantics' 07},
editor = {Pellegrini, Tassilo and Schaffert, Sebastian},
interhash = {1acc5f78c84ea7de0cc50dc3c1e4e994},
intrahash = {baf236eafcb9b39d34339a798bfef58b},
issn = {0948-6968},
pages = {297-304},
publisher = {JUCS},
title = {ConTag: A semantic tag recommendation system},
url = {http://www.dfki.uni-kl.de/~sauermann/papers/horak+2007a.pdf},
year = 2007
}
@inproceedings{musto2010combining,
abstract = {The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users' mental model and also allows the use of evolving vocabularies. However, since tags are handled in a purely syntactical way, the annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness of tag-based approaches for complex tasks. Consequently, systems called tag recommenders recently emerged, with the purpose of speeding up the so-called tag convergence, providing users with the most suitable tags for the resource to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender), which extends the social approach presented in a previous work [14] with a content-based approach able to extract tags directly from the textual content of HTML pages. Results of experiments carried out on a large dataset gathered from Bibsonomy, show that the use of content-based techniques improves the predictive accuracy of the tag recommender. },
address = {Berlin/Heidelberg},
author = {Musto, Cataldo and Narducci, Fedelucio and Lops, Pasquale and de Gemmis, Marco},
booktitle = {E-Commerce and Web Technologies},
doi = {10.1007/978-3-642-15208-5_2},
editor = {Buccafurri, Francesco and Semeraro, Giovanni},
interhash = {60254c70491f83c365ee71b019d65344},
intrahash = {bdd023e357c901c749580d038b4f2059},
isbn = {978-3-642-15207-8},
pages = {13--23},
publisher = {Springer},
series = {Lecture Notes in Business Information Processing},
title = {Combining Collaborative and Content-Based Techniques for Tag Recommendation.},
url = {http://dx.doi.org/10.1007/978-3-642-15208-5_2},
volume = 61,
year = 2010
}
@inproceedings{mishne06-autotag,
abstract = {This paper describes AutoTag, a tool which suggests tags for weblog posts using collaborative filtering methods. An evaluation of AutoTag on a large collection of posts shows good accuracy; coupled with the blogger's final quality control, AutoTag assists both in simplifying the tagging process and in improving its quality.},
address = {New York, NY, USA},
author = {Mishne, Gilad},
booktitle = {WWW '06: Proceedings of the 15th International Conference on World Wide Web},
interhash = {62d370a3abfaed3469bd2ee6a8ef6473},
intrahash = {4094419a2f26ac85175ad48207311d61},
note = {paper presented at the poster track},
pages = {953--954},
publisher = {ACM Press},
title = {AutoTag: a collaborative approach to automated tag assignment for weblog posts},
url = {http://portal.acm.org/citation.cfm?id=1135777.1135961},
year = 2006
}
@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 = {24},
volume = 6581,
year = 2011
}