Have We Really Understood Collaborative Information? An Empirical Investigation

TL;DR

This paper provides a comprehensive empirical analysis of collaborative information in recommender systems, clarifying its definition, structure, and impact on recommendation performance, thereby advancing understanding and guiding future research.

Contribution

It offers a systematic empirical investigation, including a quantitative definition, analysis of structure, and evaluation of impact of collaborative information in recommender systems.

Findings

Collaborative information is characterized by item co-occurrence patterns.

The distribution of collaborative information varies across datasets and contexts.

Collaborative information significantly influences the effectiveness of recommendation algorithms.

Abstract

Collaborative information serves as the cornerstone of recommender systems which typically focus on capturing it from user-item interactions to deliver personalized services. However, current understanding of this crucial resource remains limited. Specifically, a quantitative definition of collaborative information is missing, its manifestation within user-item interactions remains unclear, and its impact on recommendation performance is largely unknown. To bridge this gap, this work conducts a systematic investigation of collaborative information. We begin by clarifying collaborative information in terms of item co-occurrence patterns, identifying its main characteristics, and presenting a quantitative definition. We then estimate the distribution of collaborative information from several aspects, shedding light on how collaborative information is structured in practice. Furthermore, we evaluate the impact of collaborative information on the performance of various recommendation algorithms. Finally, we highlight challenges in effectively capturing collaborative information and outlook promising directions for future research. By establishing an empirical framework, we uncover many insightful observations that advance our understanding of collaborative information and offer valuable guidelines for developing more effective recommender systems.