A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com
Kirshenbuam, Evan; Forman, George; Dugan, Michael
Keyword(s): personalization; recommender systems; collaborative filtering; content analysis; live user trial
Abstract: We present the results of a multi-phase study to optimize strategies for generating personalized article recommendations at the Forbes.com web site. In the first phase we compared the performance of a variety of recommendation methods on historical data. In the second phase we deployed a live system at Forbes.com for five months on a sample of 82,000 users, each randomly assigned to one of 20 methods. We analyze the live results both in terms of click- through rate (CTR) and user session lengths. The method with the best CTR was a hybrid of collaborative-filtering and a content-based method that leverages Wikipedia-based concept features, post- processed by a novel Bayesian remapping technique that we introduce. It both statistically significantly beat decayed popularity and increased CTR by 37%.
Additional Publication Information: To be published in Proceedings of the 23rd European Conference on Machine Learning and the 15th European Conference on Principles of Data Mining and Knowledge Discovery, ECML/PKDD 2012, Bristol, UK, September 24--28, 2012; Peter Flach, Tijl De Bie, and Nello Cristianini (editors); Lecture Notes in Computer Science, Springer 2012.
External Posting Date: July 6, 2012 [Fulltext]. Approved for External Publication
Internal Posting Date: July 6, 2012 [Fulltext]