# Content-aware Neural Hashing for Cold-start Recommendation

arXiv: https://arxiv.org/abs/2006.00617 · HTML: https://mlsanity.com/paper/2006.00617/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 13 of 13)

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- [Pre-trained Recommender Systems: A Causal Debiasing Perspective](https://mlsanity.com/paper/2310.19251/) (2023)
- [HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation](https://mlsanity.com/paper/2205.12042/) (2022)
- [Representation Learning for Efficient and Effective Similarity Search and Recommendation](https://mlsanity.com/paper/2109.01815/) (2021)
- [Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading](https://mlsanity.com/paper/2109.06736/) (2021)
- [Learning Similarity Preserving Binary Codes for Recommender Systems](https://mlsanity.com/paper/2204.08569/) (2022)
- [RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction](https://mlsanity.com/paper/2210.16080/) (2022)
- [Meta-Shop: Improving Item Advertisement For Small Businesses](https://mlsanity.com/paper/2212.01414/) (2022)
- [Federated Hash Projected Latent Factor Learning](https://mlsanity.com/paper/2606.26192/) (2026)
- [Probabilistic Residual Learning for Online Recommendations](https://mlsanity.com/paper/2607.20863/) (2026)

## Abstract (arXiv)

Content-aware recommendation approaches are essential for providing meaningful recommendations for textitnew (i.e., textitcold-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.