Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences

TL;DR

This paper introduces CoVE, a framework that dynamically combines short-term and long-term user preferences using specialized models to improve personalized sequential recommendations, validated through extensive experiments on real-world datasets.

Contribution

The paper proposes a novel framework, Compositions of Variant Experts (CoVE), for integrating short- and long-term preferences in sequential recommendation systems.

Findings

Both short-term and long-term preferences significantly influence user interactions.

The CoVE framework outperforms existing methods in recommendation accuracy.

Ablation studies highlight the importance of diverse expert types.

Abstract

In the online digital realm, recommendation systems are ubiquitous and play a crucial role in enhancing user experience. These systems leverage user preferences to provide personalized recommendations, thereby helping users navigate through the paradox of choice. This work focuses on personalized sequential recommendation, where the system considers not only a user's immediate, evolving session context, but also their cumulative historical behavior to provide highly relevant and timely recommendations. Through an empirical study conducted on diverse real-world datasets, we have observed and quantified the existence and impact of both short-term (immediate and transient) and long-term (enduring and stable) preferences on users' historical interactions. Building on these insights, we propose a framework that combines short- and long-term preferences to enhance recommendation performance, namely Compositions of Variant Experts (CoVE). This novel framework dynamically integrates short- and long-term preferences through the use of different specialized recommendation models (i.e., experts). Extensive experiments showcase the effectiveness of the proposed methods and ablation studies further investigate the impact of variant expert types.