@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{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 } @inproceedings{herlocker2000explaining, abstract = {Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.}, address = {New York, NY, USA}, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Riedl, John}, booktitle = {CSCW '00: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work}, doi = {10.1145/358916.358995}, interhash = {92273b87585b39bd394cb77f5a81ff1f}, intrahash = {85b8ec0aa805890a1e82156eebdb079b}, isbn = {1-58113-222-0}, location = {Philadelphia, Pennsylvania, United States}, pages = {241--250}, publisher = {ACM}, title = {Explaining collaborative filtering recommendations}, url = {http://portal.acm.org/citation.cfm?id=358995}, year = 2000 } @incollection{schafer07, abstract = {One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.}, address = {Berlin, Heidelberg}, author = {Schafer, J. Ben and Frankowski, Dan and Herlocker, Jon and Sen, Shilad}, booktitle = {The Adaptive Web: Methods and Strategies of Web Personalization}, chapter = 9, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, file = {SpringerLink:2007/SchaferFrankowskiEtAl07p291.pdf:PDF}, interhash = {bccec4b3f6845eff4966c5cab3315509}, intrahash = {1c611c2e32fb3b735c3adcd413e95201}, isbn = {978-3-540-72078-2}, owner = {flint}, pages = {291-324}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2008.02.10}, title = {Collaborative Filtering Recommender Systems}, url = {http://dx.doi.org/10.1007/978-3-540-72079-9_9}, volume = 4321, year = 2007 }