Learning Diverse Rankings with Multi-Armed Bandits
published: Aug. 6, 2008, recorded: July 2008, views: 4869
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two new learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. We show that one of our algorithms asymptotically achieves the best possible payoff obtainable in polynomial time even as user's interests change. The other performs better empirically when user interests are static, and is still theoretically near-optimal in that case.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !