A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com
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%.
Cite
Text
Kirshenbaum et al. "A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_4Markdown
[Kirshenbaum et al. "A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/kirshenbaum2012ecmlpkdd-live/) doi:10.1007/978-3-642-33486-3_4BibTeX
@inproceedings{kirshenbaum2012ecmlpkdd-live,
title = {{A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com}},
author = {Kirshenbaum, Evan and Forman, George and Dugan, Michael},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {51-66},
doi = {10.1007/978-3-642-33486-3_4},
url = {https://mlanthology.org/ecmlpkdd/2012/kirshenbaum2012ecmlpkdd-live/}
}