Graphhash: Graph Clustering Enables Parameter Efficiency In Recommender Systems

Abstract

Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constraints at scale. To tackle this challenge, hashing techniques are often employed to map multiple entities to the same embedding and thus reduce the size of the embedding tables. Concurrently, graph-based collaborative signals have emerged as powerful tools in recommender systems, yet their potential for optimizing embedding table reduction remains unexplored. This paper introduces GraphHash, the first graph-based approach that leverages modularity-based bipartite graph clustering on user-item interaction graphs to reduce embedding table sizes. We demonstrate that the modularity objective has a theoretical connection to message-passing, which provides a foundation for our method. By employing fast clustering algorithms, GraphHash serves as a computationally efficient proxy for message-passing during preprocessing

Stats

• citations1
• S2 citations—
• github stars17
• HF likes0
• heat score4.77
• arxiv keywu2024graphhash

Related papers

• Collaborative Group-aware Hashing For Fast Recommender Systems (2025)0.00
• HS-GCN: Hamming Spatial Graph Convolutional Networks For Recommendation (2023)11.67
• Learning To Hash With Graph Neural Networks For Recommender Systems (2020)14.02
• Learning To Collide: Recommendation System Model Compression With Learned Hash Functions (2022)0.00
• Multi-probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions And Enhancing Model Freshness In Large-scale Recommenders (2026)0.00
• Learning To Hash For Recommendation: A Survey (2024)0.00
• Multi-feature Discrete Collaborative Filtering For Fast Cold-start Recommendation (2020)9.59
• Collaborative Generative Hashing For Marketing And Fast Cold-start Recommendation (2020)7.81