%0 
%0 Conference Proceedings
%A Basile, Pierpaolo; Gendarmi, Domenico; Lanubile, Filippo & Semeraro, Giovanni
%D 2007
%T Recommending Smart Tags in a Social Bookmarking System
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%B Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007)
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%P 22-29
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%F Basile2007recommending
%K bookmarking recommender social tag 
%X 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.
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%U http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/RecommendingSmartTags.pdf
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%0 Conference Proceedings
%A Firan, Claudiu S.; Nejdl, Wolfgang & Paiu, Raluca
%D 2007
%T The Benefit of Using Tag-Based Profiles
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%B LA-WEB '07: Proceedings of the 2007 Latin American Web Conference
%C Washington, DC, USA
%I IEEE Computer Society
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%P 32--41
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%F firan2007benefit
%K bookmarking profile recommender social tag 
%X 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.
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%U http://portal.acm.org/citation.cfm?id=1317537.1318430&jmp=abstract&coll=GUIDE&dl=GUIDE&CFID=66852723&CFTOKEN=79035243#abstract
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%0 Conference Proceedings
%A Heymann, Paul; Ramage, Daniel & Garcia-Molina, Hector
%D 2008
%T Social tag prediction
%E 
%B SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
%C New York, NY, USA
%I ACM
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%6
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%P 531--538
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%F heymann2008social
%K prediction recommender social tag tagging 
%X 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.
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%U http://portal.acm.org/citation.cfm?id=1390334.1390425
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%0 Journal Article
%A Naaman, Mor & Nair, Rahul
%D 2008
%T ZoneTag's Collaborative Tag Suggestions: What is This Person Doing in My Phone?
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%B IEEE MultiMedia
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%I IEEE Computer Society
%V 15
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%N 3
%P 34-40
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%F naaman2008zonetag
%K bookmarking collaborative flickr mobile recommender tag tagging zonetag 
%X We describe ZoneTag, a camera phone application that allows users to capture, annotate, and share photos directly from their phone.
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%0 
%0 Conference Proceedings
%A Sarwar, Badrul; Karypis, George; Konstan, Joseph & Reidl, John
%D 2001
%T Item-based collaborative filtering recommendation algorithms
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%B WWW '01: Proceedings of the 10th international conference on World Wide Web
%C New York, NY, USA
%I ACM
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%6
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%P 285--295
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%F sarwar2001item
%K collaborative filtering recommender tag 
%X 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.
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%U http://portal.acm.org/citation.cfm?id=372071#
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%0 Conference Proceedings
%A Sen, Shilad; Lam, Shyong K.; Rashid, Al Mamunur; Cosley, Dan; Frankowski, Dan; Osterhouse, Jeremy; Harper, F. Maxwell & Riedl, John
%D 2006
%T tagging, communities, vocabulary, evolution
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%B CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
%C New York, NY, USA
%I ACM
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%P 181--190
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%F sen2006tagging
%K bookmarking communities evolution folksonomy recommender social tag tagging vocabulary 
%X 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.
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%U http://portal.acm.org/citation.cfm?id=1180904
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%0 Conference Proceedings
%A Sigurbjörnsson, Börkur & van Zwol, Roelof
%D 2008
%T Flickr tag recommendation based on collective knowledge
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%B WWW '08: Proceeding of the 17th International Conference on World Wide Web
%C New York, NY, USA
%I ACM
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%P 327--336
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%F sigurbjoernsson2008flickr
%K bookmarking flickr recommender social tag tagging 
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%U http://portal.acm.org/citation.cfm?id=1367497.1367542
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%0 Conference Proceedings
%A Sood, Sanjay; Owsley, Sara; Hammond, Kristian & Birnbaum, Larry
%D 2007
%T TagAssist: Automatic Tag Suggestion for Blog Posts
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%B Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007)
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%F sood2007tagassist
%K blog recommender suggestion tag tagassist tagging 
%X 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.
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%U http://icwsm.org/papers/2--Sood-Owsley-Hammond-Birnbaum.pdf
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%0 Conference Proceedings
%A Tso-Sutter, Karen H. L.; Marinho, Leandro Balby & Schmidt-Thieme, Lars
%D 2008
%T Tag-aware recommender systems by fusion of collaborative filtering algorithms
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%B SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
%C New York, NY, USA
%I ACM
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%P 1995--1999
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%F tso2008tag
%K bookmarking collaborative filtering recommender social tag tagging 
%X 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.
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%U http://portal.acm.org/citation.cfm?id=1364171
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%0 Conference Proceedings
%A Xu, Yanfei; Zhang, Liang & Liu, Wei
%D 2006
%T Cubic Analysis of Social Bookmarking for Personalized Recommendation
%E Zhou, Xiaofang; Li, Jianzhong; Shen, Heng Tao; Kitsuregawa, Masaru & Zhang, Yanchun
%B APWeb
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%I Springer
%V 3841
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%P 733--738
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%S Lecture Notes in Computer Science
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%F yanfei2006cubic
%K folksonomy recommender tag 
%X 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 users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER -
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%U http://dx.doi.org/10.1007/11610113_66
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