@inproceedings{parra2009evaluation, abstract = {Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. }, author = {Parra, Denis and Brusilovsky, Peter}, booktitle = {Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web}, interhash = {03a51e24ecab3ad66fcc381980144fea}, intrahash = {42773258c36ccf2f59749991518d1784}, issn = {1613-0073}, location = {Torino, Italy}, month = jun, series = {CEUR Workshop Proceedings}, title = {Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike}, url = {http://ceur-ws.org/Vol-467/paper5.pdf}, volume = 467, year = 2009 } @inproceedings{parra2009evaluation, abstract = {Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. }, author = {Parra, Denis and Brusilovsky, Peter}, booktitle = {Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web}, interhash = {03a51e24ecab3ad66fcc381980144fea}, intrahash = {42773258c36ccf2f59749991518d1784}, issn = {1613-0073}, location = {Torino, Italy}, month = jun, series = {CEUR Workshop Proceedings}, title = {Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike}, url = {http://ceur-ws.org/Vol-467/paper5.pdf}, volume = 467, year = 2009 } @inproceedings{lee2010using, abstract = {This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study.}, acmid = {1864752}, address = {New York, NY, USA}, author = {Lee, Danielle H. and Brusilovsky, Peter}, booktitle = {Proceedings of the fourth ACM conference on Recommender systems}, doi = {10.1145/1864708.1864752}, interhash = {6fd1cbcfd94da174c910d9144467372a}, intrahash = {ec592568ca4a9f6b2ebaf41816af1ebc}, isbn = {978-1-60558-906-0}, location = {Barcelona, Spain}, numpages = {4}, pages = {221--224}, publisher = {ACM}, title = {Using self-defined group activities for improving recommendations in collaborative tagging systems}, url = {http://doi.acm.org/10.1145/1864708.1864752}, year = 2010 } @inproceedings{freyne2007collecting, abstract = {The goal of this paper is to detail the integration of two "social Web" technologies - social search and social navigation - and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.}, acmid = {1216312}, address = {New York, NY, USA}, author = {Freyne, Jill and Farzan, Rosta and Brusilovsky, Peter and Smyth, Barry and Coyle, Maurice}, booktitle = {Proceedings of the 12th international conference on Intelligent user interfaces}, doi = {10.1145/1216295.1216312}, interhash = {871e012dc7b1c131d32480f1e3a655e7}, intrahash = {93fecd064cd42e0ea5f9dc06a9458d3c}, isbn = {1-59593-481-2}, location = {Honolulu, Hawaii, USA}, numpages = {10}, pages = {52--61}, publisher = {ACM}, title = {Collecting community wisdom: integrating social search \& social navigation}, url = {http://doi.acm.org/10.1145/1216295.1216312}, year = 2007 } @inproceedings{farzan2007communitybased, 0 = {http://150.140.18.219:9090/myreview/FILES/p12.pdf}, at = {2007-06-28 13:13:26}, author = {Farzan, Rosta and Brusilovsky, Peter}, booktitle = {Proceedings of SociUM Workshop ( 1st Workshop on "Adaptation and Personalisation in Social Systems: Groups, Teams, Communities") at UM2007}, id = {1419451}, interhash = {e66ee668ed6e028b15fe8b5d0dce8331}, intrahash = {4fc9b609ab502f3db52bc304956e4804}, priority = {3}, title = {Community-based Conference Navigator}, url = {http://150.140.18.219:9090/myreview/FILES/p12.pdf}, year = 2007 } @article{citeulike:8506476, abstract = {{Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.}}, address = {Los Alamitos, CA, USA}, author = {Santander, Denis P. and Brusilovsky, Peter}, citeulike-article-id = {8506476}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2010.261}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, doi = {10.1109/WI-IAT.2010.261}, interhash = {dd320da969151c01cf270976c0803274}, intrahash = {2c8764f2fe11ef1ae43fc0a5b51301ae}, isbn = {978-0-7695-4191-4}, journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, pages = {136--142}, posted-at = {2011-01-05 00:19:36}, priority = {0}, publisher = {IEEE Computer Society}, title = {{Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}}, url = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, volume = 1, year = 2010 } @inproceedings{bateman2007applying, abstract = {This paper outlines our experiences with applying collaborative tagging in e-learning systems to supplement more traditional metadata gathering approaches. Over the last 10 years, the learning object paradigm has emerged in e-learning and has caused standards bodies to focus on creating metadata repositories based upon strict domain-free taxonomies. We argue that the social collection phenomena and flexible metadata standards are key in collecting the kinds of metadata required for adaptable online learning. This paper takes a broad look at tagging within elearning. It first looks at the implications for tagging within the domain through an analysis of tags students provided when classifying learning objects. Next, it looks at two case studies based on novel interfaces for applying tagging. These two systems emphasize tags being applied within learning content through the use of a highlighting metaphor.}, author = {Bateman, Scott and Brooks, Christopher and McCalla, Gord and Brusilovsky, Peter}, booktitle = {Proceedings of the Workshop on Tagging and Metadata for Social Information Organization (WWW'07)}, interhash = {b726fdce6bd98ddb1b42274c87aab8c9}, intrahash = {2060090e13c91ee51b81dcf8d06da035}, title = {Applying Collaborative Tagging to E-Learning}, url = {http://www2007.org/workshops/paper_56.pdf}, year = 2007 } @incollection{citeulike:3149792, abstract = {The motivation behind many Information Retrieval systems is to identify and present relevant information to people given their current goals and needs. Learning about user preferences and access patterns recent technologies make it possible to model user information needs and adapt services to meet these needs. In previous work we have presented ASSIST, a general-purpose platform which incorporates various types of social support into existing information access systems and reported on our deployment experience in a highly goal driven environment (ACM Digital Library). In this work we present our experiences in applying ASSIST to a domain where goals are less focused and where casual exploration is more dominant; YouTube. We present a general study of YouTube access patterns and detail how the ASSIST architecture affected the access patterns of users in this domain.}, author = {Coyle, Maurice and Freyne, Jill and Brusilovsky, Peter and Smyth, Barry}, citeulike-article-id = {3149792}, doi = {http://dx.doi.org/10.1007/978-3-540-70987-9\_12}, interhash = {487512d7286ca43ca9b96ee4a0efc198}, intrahash = {f75eb556b19abd7b399f2f27ae49cb1c}, journal = {Adaptive Hypermedia and Adaptive Web-Based Systems}, pages = {93--102}, posted-at = {2008-10-13 00:16:23}, priority = {2}, title = {Social Information Access for the Rest of Us: An Exploration of Social YouTube}, url = {http://www.springerlink.com/content/6h410u3w4836v866/}, year = 2008 } @inproceedings{freyne07, address = {New York, NY, USA}, author = {Freyne, Jill and Farzan, Rosta and Brusilovsky, Peter and Smyth, Barry and Coyle, Maurice}, booktitle = {IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces}, doi = {http://doi.acm.org/10.1145/1216295.1216312}, interhash = {871e012dc7b1c131d32480f1e3a655e7}, intrahash = {88603ee0903b30dc642aebdaa6a22f93}, isbn = {1-59593-481-2}, location = {Honolulu, Hawaii, USA}, pages = {52--61}, publisher = {ACM Press}, title = {Collecting community wisdom: integrating social search \& social navigation}, url = {http://portal.acm.org/citation.cfm?id=1216312}, year = 2007 }