Year: 2011
Type: book-chapter
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 submodular bandits problem, which is an online learning setting for optimizing a general 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.
Authors: Yisong Yue, Carlos Guestrin
Institution: Carnegie Mellon University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: Office of Naval Research
Awards: G3673805741, G5728373284, G8876996369
OpenAlex ID: W2117353901