Exploration vs. Exploitation in the Information Filtering Problem
This work addresses the cold-start problem in information filtering for users with limited interaction history, though it is incremental as it builds on existing bandit methods.
The paper tackled the exploration-exploitation tradeoff in information filtering by developing a Bayesian sequential decision-making model, which was solved optimally using a decomposition to two-armed bandit problems and applied to the arXiv.org dataset, showing utility in cold-start scenarios.
We consider information filtering, in which we face a stream of items too voluminous to process by hand (e.g., scientific articles, blog posts, emails), and must rely on a computer system to automatically filter out irrelevant items. Such systems face the exploration vs. exploitation tradeoff, in which it may be beneficial to present an item despite a low probability of relevance, just to learn about future items with similar content. We present a Bayesian sequential decision-making model of this problem, show how it may be solved to optimality using a decomposition to a collection of two-armed bandit problems, and show structural results for the optimal policy. We show that the resulting method is especially useful when facing the cold start problem, i.e., when filtering items for new users without a long history of past interactions. We then present an application of this information filtering method to a historical dataset from the arXiv.org repository of scientific articles.