A Comprehensive Survey on Self-Supervised Learning for Recommendation

Abstract

Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by enhancing their comprehension of user behaviors and preferences. However, supervised learning methods encounter challenges in real-life scenarios due to data sparsity, resulting in limitations in their ability to learn representations effectively. To address this, self-supervised learning (SSL) techniques have emerged as a solution, leveraging inherent data structures to generate supervision signals without relying solely on labeled data. By leveraging unlabeled data and extracting meaningful representations, recommender systems utilizing SSL can make accurate predictions and recommendations even when confronted with data sparsity. In this paper, we provide a comprehensive review of self-supervised learning frameworks designed for recommender systems, encompassing a thorough analysis of over 170 papers. We conduct an exploration of nine distinct scenarios, enabling a comprehensive understanding of SSL-enhanced recommenders in different contexts. For each domain, we elaborate on different self-supervised learning paradigms, namely contrastive learning, generative learning, and adversarial learning, so as to present technical details of how SSL enhances recommender systems in various contexts. We consistently maintain the related open-source materials at https://github.com/HKUDS/Awesome-SSLRec-Papers.

Published as an ACM Computing Survey paper

References in corpus (46)

• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Self-supervised Graph Learning for Recommendation
• Self-supervised Learning: Generative or Contrastive
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• Explainable Recommendation: A Survey and New Perspectives
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
• Graph Self-Supervised Learning: A Survey
• Knowledge Graph Contrastive Learning for Recommendation
• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
• Hypergraph Contrastive Collaborative Filtering
• Intent Contrastive Learning for Sequential Recommendation
• Bootstrap Latent Representations for Multi-modal Recommendation
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• Heterogeneous Graph Contrastive Learning for Recommendation
• Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
• Multi-Modal Self-Supervised Learning for Recommendation
• Representation Learning with Large Language Models for Recommendation
• Contrastive Meta Learning with Behavior Multiplicity for Recommendation
• Towards Representation Alignment and Uniformity in Collaborative Filtering
• Knowledge Graph Self-Supervised Rationalization for Recommendation
• Debiased Contrastive Learning for Sequential Recommendation
• Self-Supervised Hypergraph Transformer for Recommender Systems
• Disentangled Contrastive Collaborative Filtering
• Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
• Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
• Automated Self-Supervised Learning for Recommendation
• Social Recommendation with Self-Supervised Metagraph Informax Network
• Contrastive Cross-Domain Sequential Recommendation
• CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
• Multi-view Intent Disentangle Graph Networks for Bundle Recommendation
• Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network
• SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
• Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
• Multi-behavior Self-supervised Learning for Recommendation
• RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
• RecDCL: Dual Contrastive Learning for Recommendation
• Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
• Multi-level Contrastive Learning Framework for Sequential Recommendation
• Self-Supervised Learning of Graph Neural Networks: A Unified Review
• Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems
• Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
• Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
• Pre-training Graph Neural Network for Cross Domain Recommendation