Negative Sampling in Recommendation: A Survey and Future Directions

2024-09-11

arXiv.org

AI Summary

Recommender systems face persistent challenges including filter bubbles, sparse user-item interactions, cold-start problems, and feedback loops. Existing approaches predominantly leverage positive behavioral signals while underutilizing the critical role of negative feedback in preference modeling. This survey establishes, for the first time, a taxonomy of negative sampling methodologies—categorizing over one hundred works into five paradigmatic classes. It further introduces a scenario-aware adaptation framework that elucidates the pivotal roles of negative sampling in modeling dynamic preferences, mitigating filter bubbles, and alleviating feedback bias. Finally, it identifies three emerging research frontiers: enhancing interpretability, integrating causal inference, and synergizing with large language models. By rigorously delineating the theoretical boundaries and practical implementation pathways of negative sampling, this work elevates it from an empirical engineering heuristic to a foundational paradigm in recommender system design.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Recommender systems aim to capture users' personalized preferences from the cast amount of user behaviors, making them pivotal in the era of information explosion. However, the presence of the dynamic preference, the"information cocoons", and the inherent feedback loops in recommendation 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 interest 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 recommendation. In this survey, we first discuss the role of negative sampling in recommendation and thoroughly analyze challenges that consistently impede its progress. Then, we conduct an extensive literature review on the existing negative sampling strategies in recommendation and classify them into five categories with their discrepant techniques. Finally, we detail the insights of the tailored negative sampling strategies in diverse recommendation scenarios and outline an overview of the prospective research directions toward which the community may engage and benefit.

Problem

• Addresses challenges in capturing user preferences in recommender systems
• Explores the role of negative sampling in understanding user behavior
• Surveys and categorizes negative sampling strategies for diverse scenarios

Innovation

• Negative sampling reveals genuine negative user behaviors
• Classifies negative sampling strategies into five categories
• Tailors strategies for diverse recommendation scenarios