Collaborative filtering via sparse Markov random fields

TL;DR

This paper introduces a sparse Markov random field approach for collaborative filtering in recommender systems, automatically learning interaction structures among users and items, and demonstrating effectiveness on large datasets.

Contribution

The paper presents a novel sparsity-inducing algorithm for structure learning in Markov random fields applied to collaborative filtering, enabling automatic discovery of user and item interactions.

Findings

Effective structure learning for user-item interactions

Automatic inference of user-user and item-item networks

Strong performance on movie and date matching datasets

Abstract

Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov random fields (MRF). We address the open problem of structure learning and introduce a sparsity-inducing algorithm to automatically estimate the interaction structures between users and between items. Item-item and user-user correlation networks are obtained as a by-product. Large-scale experiments on movie recommendation and date matching datasets demonstrate the power of the proposed method.