@article{kautz1997referral, acmid = {245123}, address = {New York, NY, USA}, author = {Kautz, Henry and Selman, Bart and Shah, Mehul}, doi = {10.1145/245108.245123}, interhash = {6995678b936b33eef9ea1396e53a1fc7}, intrahash = {832d16a8c86e769c7ac9ace5381f757e}, issn = {0001-0782}, issue_date = {March 1997}, journal = {Communications of the ACM}, month = mar, number = 3, numpages = {3}, pages = {63--65}, publisher = {ACM}, title = {Referral Web: combining social networks and collaborative filtering}, url = {http://doi.acm.org/10.1145/245108.245123}, volume = 40, year = 1997 } @inproceedings{gemmell2009improving, abstract = {Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.}, author = {Gemmell, Jonathan and Schimoler, Thomas R. and Christiansen, Laura and Mobasher, Bamshad}, booktitle = {ACM RecSys'09 Workshop on Recommender Systems and the Social Web}, editor = {Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Anand, Sarabjot Singh and Dugan, Casey and Mobasher, Bamshad and Kobsa, Alfred}, interhash = {0900f921d87c5ee19a4ed2c70e5a71df}, intrahash = {6b1ff3b7b691b84288fb7122968134c4}, issn = {1613-0073}, month = oct, pages = {17--24}, series = {CEUR-WS.org}, title = {Improving Folkrank With Item-Based Collaborative Filtering}, url = {http://ceur-ws.org/Vol-532/paper3.pdf}, volume = 532, 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{jaeschke07tagKdml, author = {Jaeschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {19e40fd1eb137fab091512656ecc504d}, intrahash = {71bc9f8ae1a53632dc9a2b98b017f152}, isbn = {978-3-86010-907-6}, month = sep, pages = {13-20}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Tag Recommendations in Folksonomies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf}, year = 2007 } @inproceedings{jaeschke07tagKdml, author = {Jaeschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {19e40fd1eb137fab091512656ecc504d}, intrahash = {71bc9f8ae1a53632dc9a2b98b017f152}, isbn = {978-3-86010-907-6}, month = sep, pages = {13-20}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Tag Recommendations in Folksonomies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf}, year = 2007 } @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{jaeschke07tagKdml, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {7e212e3bac146d406035adebff248371}, intrahash = {bfc43dfe59f9c0935ac3364b12e6d795}, isbn = {978-3-86010-907-6}, month = sep, pages = {13-20}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Tag Recommendations in Folksonomies}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf}, vgwort = {20}, year = 2007 } @inproceedings{Byde2007, 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}, priority = {5}, title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics}, url = {http://www.icwsm.org/papers/paper47.html}, year = 2007 } @article{245123, address = {New York, NY, USA}, author = {Kautz, Henry and Selman, Bart and Shah, Mehul}, doi = {http://doi.acm.org/10.1145/245108.245123}, interhash = {6995678b936b33eef9ea1396e53a1fc7}, intrahash = {ba3606b3aa6c4cf94784db451b28cd68}, issn = {0001-0782}, journal = {Commun. ACM}, number = 3, pages = {63--65}, publisher = {ACM Press}, title = {Referral Web: combining social networks and collaborative filtering}, volume = 40, year = 1997 } @article{245123, address = {New York, NY, USA}, author = {Kautz, Henry and Selman, Bart and Shah, Mehul}, doi = {http://doi.acm.org/10.1145/245108.245123}, interhash = {6995678b936b33eef9ea1396e53a1fc7}, intrahash = {ba3606b3aa6c4cf94784db451b28cd68}, issn = {0001-0782}, journal = {Commun. ACM}, number = 3, pages = {63--65}, publisher = {ACM Press}, title = {Referral Web: combining social networks and collaborative filtering}, volume = 40, year = 1997 }