Adversarial Online Collaborative Filtering

TL;DR

This paper introduces adaptive algorithms for online collaborative filtering under no-repetition constraints, providing optimal regret guarantees for biclustered and general preference matrices, with empirical validation on real datasets.

Contribution

It presents the first adaptive, parameter-free algorithms with regret guarantees for online collaborative filtering under no-repetition constraints, applicable to both biclustered and general matrices.

Findings

Algorithms achieve optimal regret guarantees.

Robust algorithms perform well on real-world datasets.

Empirical results show advantages over standard baselines.

Abstract

We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot be recommended the same content item more than once. We start by designing and analyzing an algorithm that works under biclustering assumptions on the user-item preference matrix, and show that this algorithm exhibits an optimal regret guarantee, while being fully adaptive, in that it is oblivious to any prior knowledge about the sequence of users, the universe of items, as well as the biclustering parameters of the preference matrix. We then propose a more robust version of this algorithm which operates with general matrices. Also this algorithm is parameter free, and we prove regret guarantees that scale with the amount by which the preference matrix deviates from a biclustered structure. To our knowledge, these are the first results on online collaborative filtering that hold at this level of generality and adaptivity under no-repetition constraints. Finally, we complement our theoretical findings with simple experiments on real-world datasets aimed at both validating the theory and empirically comparing to standard baselines. This comparison shows the competitive advantage of our approach over these baselines.