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Content-aware Neural Hashing for Cold-start Recommendation | | Casper Hansen
; Christian Hansen
; Jakob Grue Simonsen
; Stephen Alstrup
; Christina Lioma
; | | Date: |
31 May 2020 | | Abstract: | Content-aware recommendation approaches are essential for providing
meaningful recommendations for extit{new} (i.e., extit{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. | | Source: | arXiv, 2006.0617 | | Services: | Forum | Review | PDF | Favorites | |
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