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Return to Session VIIIa: Content-based filtering & collaborative filtering
Collaborative Filtering (CF) systems have been researched for over a decade as a tool to deal with information overload. At the heart of these systems are the algorithms which generate the predictions and recommendations. In this article we empirically demonstrate that two of the most acclaimed CF recommendation algorithms have flaws that result in a dramatically unacceptable user experience. In response, we introduce a new Belief Distribution Algorithm that overcomes these flaws and provides substantially richer user modeling. The Belief Distribution Algorithm retains the qualities of nearest-neighbor algorithms which have performed well in the past, yet produces predictions of belief distributions across rating values rather than a point rating value. In addition, we illustrate how the exclusive use of the mean absolute error metric has concealed these flaws for so long, and we propose the use of a modified Precision metric for more accurately evaluating the user experience.
@inproceedings{1009050, author = {Matthew R. McLaughlin and Jonathan L. Herlocker}, title = {A collaborative filtering algorithm and evaluation metric that accurately model the user experience}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {329--336}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009050}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
