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Miami, Florida
Dec. 6, 2009 to Dec. 9, 2009
ISBN: 978-0-7695-3895-2
pp: 1-10
ABSTRACT
We propose novel multi-armed bandit (explore/exploit) schemes to maximize total clicks on a content module published regularly on Yahoo! Intuitively, one can ``explore'' each candidate item by displaying it to a small fraction of user visits to estimate the item's click-through rate (CTR), and then ``exploit'' high CTR items in order to maximize clicks. While bandit methods that seek to find the optimal trade-off between explore and exploit have been studied for decades, existing solutions are not satisfactory for web content publishing applications where dynamic set of items with short lifetimes, delayed feedback and non-stationary reward (CTR) distributions are typical. In this paper, we develop a Bayesian solution and extend several existing schemes to our setting. Through extensive evaluation with nine bandit schemes, we show that our Bayesian solution is uniformly better in several scenarios. We also study the empirical characteristics of our schemes and provide useful insights on the strengths and weaknesses of each. Finally, we validate our results with a ``side-by-side'' comparison of schemes through live experiments conducted on a random sample of real user visits to Yahoo!
INDEX TERMS
multi-armed bandits, web application, content scheduling, Bayes optimal
CITATION
Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, "Explore/Exploit Schemes for Web Content Optimization", ICDM, 2009, 2013 IEEE 13th International Conference on Data Mining, 2013 IEEE 13th International Conference on Data Mining 2009, pp. 1-10, doi:10.1109/ICDM.2009.52
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