A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Abstract

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data; 2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field.

In submission

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Cited by in corpus (22)

• Multi-View Graph Convolutional Network for Multimedia Recommendation
• A Review-aware Graph Contrastive Learning Framework for Recommendation
• Automated Self-Supervised Learning for Recommendation
• Multi-Behavior Recommendation with Cascading Graph Convolution Networks
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
• Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles
• MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
• Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
• Recommender systems based on graph embedding techniques: A comprehensive review
• Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
• A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
• Embedding Compression in Recommender Systems: A Survey
• Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
• How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
• Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
• Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
• Multi-Cause Deconfounding for Recommender Systems with Latent Confounders
• User Invariant Preference Learning for Multi-Behavior Recommendation
• Mitigating Spurious Correlations for Self-supervised Recommendation
• Intent Propagation Contrastive Collaborative Filtering
• Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
• Searching Personal Collections