The Bayesian Linear Information Filtering Problem

TL;DR

This paper formulates the information filtering problem as a Bayesian sequential decision process, introduces new heuristic policies, and demonstrates their near-optimal performance through theoretical bounds and empirical evaluation.

Contribution

It introduces a Bayesian linear model for user preferences, develops new heuristic policies, and provides a computational upper bound to evaluate their optimality gap.

Findings

DTD-UCB and DTD-DP outperform benchmarks

Performance of heuristics is close to the theoretical upper bound

Significant improvement demonstrated on real and simulated data

Abstract

We present a Bayesian sequential decision-making formulation of the information filtering problem, in which an algorithm presents items (news articles, scientific papers, tweets) arriving in a stream, and learns relevance from user feedback on presented items. We model user preferences using a Bayesian linear model, similar in spirit to a Bayesian linear bandit. We compute a computational upper bound on the value of the optimal policy, which allows computing an optimality gap for implementable policies. We then use this analysis as motivation in introducing a pair of new Decompose-Then-Decide (DTD) heuristic policies, DTD-Dynamic-Programming (DTD-DP) and DTD-Upper-Confidence-Bound (DTD-UCB). We compare DTD-DP and DTD-UCB against several benchmarks on real and simulated data, demonstrating significant improvement, and show that the achieved performance is close to the upper bound.