Recommendation Through Mixtures of Heterogeneous Item Relationships

TL;DR

This paper introduces a framework that combines multiple heterogeneous item relationship signals in recommender systems, improving accuracy and providing interpretable explanations by modeling user susceptibility and optimal recommendations across different modalities.

Contribution

It develops a novel mixture-of-experts framework that integrates diverse item relationship signals and models user preferences dynamically, enhancing recommendation quality and interpretability.

Findings

Achieves higher recommendation accuracy than existing methods.

Provides intuitive explanations for recommendations.

Effectively models user susceptibility to different signal modalities.

Abstract

Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data. Specific instances make use of signals ranging from user feedback, item relationships, geographic locality, social influence (etc.). Typically, research proceeds by showing that making use of a specific signal (within a carefully designed model) allows for higher-fidelity recommendations on a particular dataset. Of course, the real situation is more nuanced, in which a combination of many signals may be at play, or favored in different proportion by individual users. Here we seek to develop a framework that is capable of combining such heterogeneous item relationships by simultaneously modeling (a) what modality of recommendation is a user likely to be susceptible to at a particular point in time; and (b) what is the best recommendation from each modality. Our method borrows ideas from mixtures-of-experts approaches as well as knowledge graph embeddings. We find that our approach naturally yields more accurate recommendations than alternatives, while also providing intuitive `explanations' behind the recommendations it provides.