Negative Sampling in Recommendation: A Survey and Future Directions

TL;DR

This paper surveys negative sampling techniques in recommender systems, emphasizing their importance in capturing negative user feedback, analyzing existing strategies, challenges, and future research directions.

Contribution

It provides a comprehensive classification and analysis of negative sampling methods in RS, highlighting challenges and proposing future research directions.

Findings

Negative sampling effectively reveals genuine negative user preferences.

Existing strategies are classified into five categories with distinct techniques.

Future directions include tailored negative sampling for diverse RS scenarios.

Abstract

Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of ``information cocoons'', interaction sparsity, cold-start problem and feedback loops inherent in RS make users interact with a limited number of items. Conventional recommendation algorithms typically focus on the positive historical behaviors, while neglecting the essential role of negative feedback in user preference understanding. As a promising but easy-to-ignored area, negative sampling is proficients in revealing the genuine negative aspect inherent in user behaviors, emerging as an inescapable procedure in RS. In this survey, we first discuss existing user feedback, the critical role of negative sampling and the optimization objectives in RS and thoroughly analyze challenges that consistently impede its progress. Then, we conduct an extensive literature review on the existing negative sampling strategies in RS and classify them into five categories with their discrepant techniques. Finally, we detail the insights of the tailored negative sampling strategies in diverse RS scenarios and outline an overview of the prospective research directions toward which the community may engage and benefit.