@inproceedings{karypis2001evaluation, abstract = {The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based Collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions. To address these scalability concerns item-based recommendation techniques have been developed that analyze the user-item matrix to identify relations between the different items, and use these relations to compute the list of recommendations.In this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various items and then used them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.}, acmid = {502627}, address = {New York, NY, USA}, author = {Karypis, George}, booktitle = {Proceedings of the Tenth International Conference on Information and Knowledge Management}, doi = {10.1145/502585.502627}, interhash = {ad804add9a1dec7cb4df3c98fac7dc13}, intrahash = {234c68832d68a4530e3ba8e2fb533043}, isbn = {1-58113-436-3}, location = {Atlanta, Georgia, USA}, numpages = {8}, pages = {247--254}, publisher = {ACM}, series = {CIKM '01}, title = {Evaluation of Item-Based Top-N Recommendation Algorithms}, url = {http://doi.acm.org/10.1145/502585.502627}, year = 2001 } @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.}, 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 = {6190d6064dfdb3b8d71f2898539e993e}, note = {accepted for publication}, pages = {New York, NY, USA}, publisher = {ACM}, title = {Tag Recommendations for SensorFolkSonomies}, year = 2013 } @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 } @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.}, 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 = {6190d6064dfdb3b8d71f2898539e993e}, note = {accepted for publication}, pages = {New York, NY, USA}, publisher = {ACM}, title = {Tag Recommendations for SensorFolkSonomies}, year = 2013 } @article{jaschke2008recommendations, 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 occurrences. 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.}, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, doi = {10.3233/AIC-2008-0438}, interhash = {b2f1aba6829affc85d852ea93a8e39f7}, intrahash = {f16901fa31de8394c0a8ce6f03f29ff2}, journal = {AI Communications}, month = jan, number = 4, pages = {231--247}, title = {Tag recommendations in social bookmarking systems}, url = {http://dx.doi.org/10.3233/AIC-2008-0438}, volume = 21, year = 2008 } @inproceedings{gemmell2010hybrid, abstract = {Social annotation systems allow users to annotate resources with personalized tags and to navigate large and complex information spaces without the need to rely on predefined hierarchies. These systems help users organize and share their own resources, as well as discover new ones annotated by other users. Tag recommenders in such systems assist users in finding appropriate tags for resources and help consolidate annotations across all users and resources. But the size and complexity of the data, as well as the inherent noise and inconsistencies in the underlying tag vocabularies, have made the design of effective tag recommenders a challenge. Recent efforts have demonstrated the advantages of integrative models that leverage all three dimensions of a social annotation system: users, resources and tags. Among these approaches are recommendation models based on matrix factorization. But, these models tend to lack scalability and often hide the underlying characteristics, or "information channels" of the data that affect recommendation effectiveness. In this paper we propose a weighted hybrid tag recommender that blends multiple recommendation components drawing separately on complementary dimensions, and evaluate it on six large real-world datasets. In addition, we attempt to quantify the strength of the information channels in these datasets and use these results to explain the performance of the hybrid. We find our approach is not only competitive with the state-of-the-art techniques in terms of accuracy, but also has the added benefits of being scalable to large real world applications, extensible to incorporate a wide range of recommendation techniques, easily updateable, and more scrutable than other leading methods.}, acmid = {1871543}, address = {New York, NY, USA}, author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin}, booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management}, doi = {10.1145/1871437.1871543}, interhash = {e0020596af50b5d01735acd3d76d3fa1}, intrahash = {9836f538c642c9cff810edba87993d2c}, isbn = {978-1-4503-0099-5}, location = {Toronto, ON, Canada}, numpages = {10}, pages = {829--838}, publisher = {ACM}, series = {CIKM '10}, title = {Hybrid tag recommendation for social annotation systems}, url = {http://doi.acm.org/10.1145/1871437.1871543}, year = 2010 } @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{shani2011evaluating, author = {Shani, G. and Gunawardana, A.}, interhash = {c93599e113544cde3f44502c88775c20}, intrahash = {63a1a401a35be851b9864966184c6815}, journal = {Recommender Systems Handbook}, pages = {257--297}, publisher = {Springer}, title = {Evaluating recommendation systems}, url = {http://scholar.google.de/scholar.bib?q=info:AW2lmZl44hMJ:scholar.google.com/&output=citation&hl=de&as_sdt=0,5&ct=citation&cd=0}, year = 2011 } @article{zhang2012combining, abstract = {Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.}, author = {Zhang, Yin and Zhang, Bin and Gao, Kening and Guo, Pengwei and Sun, Daming}, doi = {10.1016/j.physa.2012.05.013}, interhash = {088ad59c786579d399aaee48db5e6a7a}, intrahash = {84f824839090a5e20394b85a9e1cef08}, issn = {0378-4371}, journal = {Physica A: Statistical Mechanics and its Applications}, number = 22, pages = {5759 - 5768}, title = {Combining content and relation analysis for recommendation in social tagging systems}, url = {http://www.sciencedirect.com/science/article/pii/S0378437112003846}, volume = 391, year = 2012 } @article{zhang2010personalized, abstract = {Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.}, author = {Zhang, Zi-Ke and Zhou, Tao and Zhang, Yi-Cheng}, doi = {10.1016/j.physa.2009.08.036}, interhash = {caa341f4d9ffb507dbf72f75a201dbd1}, intrahash = {8fc27ade71ea065b92874ba29fca711b}, issn = {0378-4371}, journal = {Physica A: Statistical Mechanics and its Applications}, number = 1, pages = {179 - 186}, title = {Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs}, url = {http://www.sciencedirect.com/science/article/pii/S0378437109006839}, volume = 389, year = 2010 } @inproceedings{kim2011personalized, abstract = {This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.}, acmid = {2043945}, address = {New York, NY, USA}, author = {Kim, Heung-Nam and El Saddik, Abdulmotaleb}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, doi = {10.1145/2043932.2043945}, interhash = {1004b267b14d0abde0f8ac3a7ceadd38}, intrahash = {f022e60c5928e01c701d7ec539ec221b}, isbn = {978-1-4503-0683-6}, location = {Chicago, Illinois, USA}, numpages = {8}, pages = {45--52}, publisher = {ACM}, series = {RecSys '11}, title = {Personalized PageRank vectors for tag recommendations: inside FolkRank}, url = {http://doi.acm.org/10.1145/2043932.2043945}, year = 2011 } @incollection{reference/rsh/ShaniG11, author = {Shani, Guy and Gunawardana, Asela}, 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_8}, interhash = {c93599e113544cde3f44502c88775c20}, intrahash = {2eb0452b940a466b9b984abb2aa610b3}, isbn = {978-0-387-85819-7}, pages = {257-297}, publisher = {Springer}, title = {Evaluating Recommendation Systems.}, url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html#ShaniG11}, year = 2011 } @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.}, acmid = {1454048}, address = {New York, NY, USA}, author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin}, booktitle = {Proceedings of the 2008 ACM conference on Recommender systems}, doi = {10.1145/1454008.1454048}, interhash = {c9028129dd7cd8314673bd64cbb6198e}, intrahash = {0700627147554148d7e6db5979aa27d2}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, numpages = {8}, pages = {259--266}, publisher = {ACM}, series = {RecSys '08}, title = {Personalized recommendation in social tagging systems using hierarchical clustering}, url = {http://doi.acm.org/10.1145/1454008.1454048}, year = 2008 } @inproceedings{heck2011testing, author = {Heck, Tamara and Peters, Isabella and Stock, Wolfgang G.}, booktitle = {Workshop on Recommender Systems and the Social Web (ACM RecSys'11)}, interhash = {d250a0eb45ca7c198d9cdb238802fd74}, intrahash = {8b68db4ae61ec5c97010fbec2ddaa6c6}, title = {Testing collaborative filtering against co-citation analysis and bibliographic coupling for academic author recommendation}, 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 = {Kacprzyk, Janusz and Jain, Lakhmi C.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {b645572f4c635b2d674126f26d4303d6}, 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 } @article{Herlocker:2004:ECF:963770.963772, abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.}, acmid = {963772}, address = {New York, NY, USA}, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.}, doi = {10.1145/963770.963772}, interhash = {f8a70731d983634ac7105896d101c9d2}, intrahash = {c3a659108a568db1fba183c680dd1fd2}, issn = {1046-8188}, issue = {1}, journal = {ACM Trans. Inf. Syst.}, month = {January}, numpages = {49}, pages = {5--53}, publisher = {ACM}, title = {Evaluating collaborative filtering recommender systems}, url = {http://doi.acm.org/10.1145/963770.963772}, volume = 22, year = 2004 } @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/sigir/GuanBMCW09, author = {Guan, Ziyu and Bu, Jiajun and Mei, Qiaozhu and Chen, Chun and Wang, Can}, booktitle = {SIGIR}, crossref = {conf/sigir/2009}, editor = {Allan, James and Aslam, Javed A. and Sanderson, Mark and Zhai, ChengXiang and Zobel, Justin}, ee = {http://doi.acm.org/10.1145/1571941.1572034}, interhash = {53d2e8bc966048bc01efcc57b2fc8250}, intrahash = {ac9427acf51cbf7cb5a35f66a16a32c0}, isbn = {978-1-60558-483-6}, pages = {540-547}, publisher = {ACM}, title = {Personalized tag recommendation using graph-based ranking on multi-type interrelated objects.}, url = {http://www-personal.umich.edu/~qmei/pub/sigir09-tag.pdf}, year = 2009 } @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 = {http://doi.acm.org/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 } @proceedings{gunawardana2009survey, author = {Gunawardana, Asela and Shani, Guy}, interhash = {441df9b673faf85aecc45babd8883069}, intrahash = {49600df05a884106989d71dedcaa7e1b}, page = {2935−2962}, title = { A Survey of Accuracy Evaluation Metrics of Recommendation Tasks }, url = {http://jmlr.csail.mit.edu/papers/v10/gunawardana09a.html}, volume = {v10}, year = 2009 }