Embedding In Recommender Systems: A Survey

Abstract

Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that convert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors, which can enhance the recommendation performance. Embedding techniques have revolutionized the capture of complex entity relationships, generating significant research interest. This survey presents a comprehensive analysis of recent advances in recommender system embedding techniques. We examine centralized embedding approaches across matrix, sequential, and graph structures. In matrix-based scenarios, collaborative filtering generates embeddings that effectively model user-item preferences, particularly in sparse data environments. For sequential data, we explore various approaches including recurrent neural networks and self-supervised methods such as contrastive and generative learning. In graph

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keywang2023embedding

Related papers

• Mixed-precision Embeddings For Large-scale Recommendation Models (2024)0.00
• Unified Semantic And ID Representation Learning For Deep Recommenders (2025)0.00
• Divide And Conquer: Towards Better Embedding-based Retrieval For Recommender Systems From A Multi-task Perspective (2023)7.16
• Collaborative Similarity Embedding For Recommender Systems (2019)13.93
• Autoemb: Automated Embedding Dimensionality Search In Streaming Recommendations (2020)12.61
• Beyond Similarity: Relation Embedding With Dual Attentions For Item-based Recommendation (2019)0.00
• Pfeed: Generating Near Real-time Personalized Feeds Using Precomputed Embedding Similarities (2024)0.00
• Embedding-based Recommender System For Job To Candidate Matching On Scale (2021)0.00