@inproceedings{melville2002contentboosted, abstract = {Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.}, acmid = {777124}, address = {Menlo Park, CA, USA}, author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass}, booktitle = {Eighteenth National Conference on Artificial Intelligence}, interhash = {985028099c1a29f116ad7434005895ac}, intrahash = {a4917f0299f48e403966a8003ebd50be}, isbn = {0-262-51129-0}, location = {Edmonton, Alberta, Canada}, numpages = {6}, pages = {187--192}, publisher = {American Association for Artificial Intelligence}, title = {Content-boosted Collaborative Filtering for Improved Recommendations}, url = {http://dl.acm.org/citation.cfm?id=777092.777124}, year = 2002 } @inproceedings{sarwar2001itembased, acmid = {372071}, address = {New York, NY, USA}, author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John}, booktitle = {Proceedings of the 10th international conference on World Wide Web}, doi = {10.1145/371920.372071}, interhash = {043d1aaba0f0b8c01d84edd517abedaf}, intrahash = {16f38785d7829500ed41c610a5eff9a2}, isbn = {1-58113-348-0}, location = {Hong Kong, Hong Kong}, numpages = {11}, pages = {285--295}, publisher = {ACM}, title = {Item-based collaborative filtering recommendation algorithms}, url = {http://doi.acm.org/10.1145/371920.372071}, year = 2001 } @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{koren2009collaborative, abstract = {Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.}, acmid = {1557072}, address = {New York, NY, USA}, author = {Koren, Yehuda}, booktitle = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, doi = {10.1145/1557019.1557072}, interhash = {ca14b78afaf26db8dd7eb13d7986830a}, intrahash = {dad3f9050f58acf0551924e537e84e45}, isbn = {978-1-60558-495-9}, location = {Paris, France}, numpages = {10}, pages = {447--456}, publisher = {ACM}, title = {Collaborative filtering with temporal dynamics}, url = {http://doi.acm.org/10.1145/1557019.1557072}, year = 2009 } @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 } @article{takacs2009scalable, abstract = {The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.}, acmid = {1577091}, author = {Takács, Gábor and Pilászy, István and Németh, Bottyán and Tikk, Domonkos}, interhash = {cd0d01e922dbd5b178e8e5b0a4d1e96c}, intrahash = {1f1be967aed57e6e42a5d99ca98584cd}, issn = {1532-4435}, journal = {Journal of Machine Learning Research}, month = jun, numpages = {34}, pages = {623--656}, publisher = {JMLR.org}, title = {Scalable Collaborative Filtering Approaches for Large Recommender Systems}, url = {http://dl.acm.org/citation.cfm?id=1577069.1577091}, volume = 10, 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{ls_leimeister, address = {Rome, Italy}, author = {Blohm, I. and Ott, F. and Bretschneider, U. and Huber, M. and Rieger, M. and Glatz, F. and Koch, M. and Leimeister, J. M. and Krcmar, H.}, booktitle = {10. European Academy of Management Conference (EURAM) 2010}, interhash = {dfecc9fb1946790fa7e081efbe748002}, intrahash = {436434c956e0b059895bc4fe28b58930}, note = {197 (45-10) }, number = 10, title = {Extending Open Innovation Platforms into the real world - Using Large Displays in Public Spaces}, url = {http://www.uni-kassel.de/fb7/ibwl/leimeister/pub/JML_197.pdf}, year = 2010 }