Title: Linear submodular bandits and their application to diversified retrieva

Authors: Y. Yue and C. Guestrin

Abstract

Diversified retrieval and online learning are two core research areas in the design of modern information retrieval systems. In this paper, we propose the linear sub- modular bandits problem, which is an online learning setting for optimizing a gen- eral class of feature-rich submodular utility models for diversified retrieval. We present an algorithm, called LSBGREEDY, and prove that it efficiently converges to a near-optimal model. As a case study, we applied our approach to the setting of personalized news recommendation, where the system must recommend small sets of news articles selected from tens of thousands of available articles each day. In a live user study, we found that LSBGREEDY significantly outperforms existing online learning approaches.

Full Text: [PDF]

Accessibility at Yale   Inference, Information, and Decision Group at Yale