Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2025

Abstract

Social recommendation, which leverages users’ social information to predict users’ preferences, is a popular branch of recommender systems. Many existing studies have attempted to advance the performance of collaborative filtering methods by leveraging the user-user matrix to enhance user embedding learning with user’s social connections. While the existing social recommender systems have demonstrated good performance in various recommendation tasks, the extent of social information usefulness in recommender systems remains unclear. This paper addresses the research gap by designing experiments to answer three research questions: (i) How useful is social information in varying user-item data sparsity? (ii) How much social information do the existing social recommendation models use? (iii) How valuable is social information for cold-start situations? Working towards answering the research questions, we introduce evaluation metrics to estimate the utilization of social information in the existing social recommendation models. We conducted experiments on three publicly available social recommendation datasets, and our results showed that there are diminishing returns when applying social information in recommender systems.

Keywords

Social Recommendation, Recommendation Evaluation Metrics, Recommender Systems, Social Information Utilization

Discipline

Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

CIKM 2025: Proceedings of the 34th ACM International Conference on Information and Knowledge Management, Seoul, Korea, November 10-14

First Page

2105

Last Page

2114

Identifier

10.1145/3746252.3761168

Publisher

ACM

City or Country

New York

Additional URL

https://dl.acm.org/doi/10.1145/3746252.3761168

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