Content-aware Neural Hashing for Cold-start Recommendation

TL;DR

NeuHash-CF is a neural hashing method for cold-start recommendation that generates compact binary codes for users and items, improving accuracy and efficiency over existing models by leveraging content information and joint autoencoder architecture.

Contribution

It introduces a unified content-aware neural hashing approach for cold-start recommendation, generating user and item hash codes within a single autoencoder framework, unlike previous methods.

Findings

Outperforms state-of-the-art baselines by up to 12% NDCG and 13% MRR in cold-start scenarios.

Uses 2-4x shorter hash codes with comparable or better performance.

Achieves significant storage reduction while maintaining high recommendation accuracy.

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 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.