A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com

摘要

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%.