Content-aware Neural Hashing For Cold-start Recommendation

Abstract

Content-aware recommendation approaches are essential for providing meaningful recommendations for \textit\{new\} (i.e., \textit\{cold-start\}) items in a recommender system. We present a content-aware neural hashing-based collaborative filtering approach (NeuHash-CF), which generates binary hash codes for users and items, such that the highly efficient Hamming distance can be used for estimating user-item relevance. NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes. Inspired from semantic hashing, the item hashing component generates a hash code directly from an item's content information (i.e., it generates cold-start and seen item hash codes in the same manner). This contrasts existing state-of-the-art models, which treat the two item cases separately. The user hash codes are generated directly based on user id, through learning a user embedding matrix. We show experimentally that NeuHash-CF sign

Stats

• citations28
• S2 citations—
• github stars0
• HF likes0
• heat score10.97
• arxiv keyhansen2020content

Related papers

• 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
• Deep Pairwise Hashing For Cold-start Recommendation (2020)6.34
• Collaborative Group-aware Hashing For Fast Recommender Systems (2025)0.00
• HS-GCN: Hamming Spatial Graph Convolutional Networks For Recommendation (2023)11.67
• Learning Similarity Preserving Binary Codes For Recommender Systems (2022)0.00
• Learning To Hash For Recommendation: A Survey (2024)0.00
• Zero-shot Heterogeneous Transfer Learning From Recommender Systems To Cold-start Search Retrieval (2020)10.85