Abstract: Current recommendation algorithms based on hashing technology commonly uses Hamming distance to indicate the similarity between user hash code and item hash code,while it ignores the potential difference information of each bit dimension.Therefore,this paper proposes a differential Hamming distance,which by calculating the dissimilarity between hash codes to assign bit weights.This paper designs a variational recommendation model for dissimilarity Hamming distance.The model is divided into a user hash component and an item hash component,which are connected by variational autoencoder structure.The model uses encoder to generate hash codes for user and items.In order to improve the robustness of the hash codes,we apply a Gaussian noise to both user and item hash coeds.Besides,the user and item hash codes are optimized by differential Hamming distance to maximize the ability of the model to reconstruct user-item scores.Experiments on benchmark datasets demonstrate that the proposed algorithm VDHR improves 3.9% in NDCG and 4.7% in MRR compared to the state-of-the-art hash recommendation algorithm under the premise of constant computational cost.

Key words: Hamming distance, Differential Hamming distance, Bit weights, Recommendation algorithm, Variational autoencoder