Content-aware Neural Hashing for Cold-start Recommendation
| Autor: | Christina Lioma, Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Information retrieval Computer science cold-start recommendation Hash function Hamming distance 02 engineering and technology content-aware recommendation Autoencoder hashing Computer Science - Information Retrieval 020204 information systems autoencoders collaborative filtering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Relevance (information retrieval) Learning to rank Information Retrieval (cs.IR) |
| Zdroj: | Hansen, C, Hansan, C, Simonsen, J G, Alstrup, S & Lioma, C 2020, Content-aware Neural Hashing for Cold-start Recommendation . in SIGIR 2020-Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . Association for Computing Machinery, pp. 971-980, 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual, Online, China, 25/07/2020 . https://doi.org/10.1145/3397271.3401060 SIGIR |
| DOI: | 10.1145/3397271.3401060 |
| Popis: | 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 significantly outperforms state-of-the-art baselines by up to 12\% NDCG and 13\% MRR in cold-start recommendation settings, and up to 4\% in both NDCG and MRR in standard settings where all items are present while training. Our approach uses 2-4x shorter hash codes, while obtaining the same or better performance compared to the state of the art, thus consequently also enabling a notable storage reduction. Accepted to SIGIR 2020 |
| Databáze: | OpenAIRE |
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