Variational Recommendation Algorithm Based on Differential Hamming Distance
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,...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-12-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-12-178.pdf |
| _version_ | 1827965459322896384 |
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| author | DONG Jia-wei, SUN Fu-zhen, WU Xiang-shuai, WU Tian-hui, WANG Shao-qing |
| author_facet | DONG Jia-wei, SUN Fu-zhen, WU Xiang-shuai, WU Tian-hui, WANG Shao-qing |
| author_sort | DONG Jia-wei, SUN Fu-zhen, WU Xiang-shuai, WU Tian-hui, WANG Shao-qing |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T17:33:21Z |
| format | Article |
| id | doaj.art-2b3fce1e84df499ba0d7a7974322aae4 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:33:21Z |
| publishDate | 2022-12-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-2b3fce1e84df499ba0d7a7974322aae42023-04-18T02:32:59ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-12-01491217818410.11896/jsjkx.220600024Variational Recommendation Algorithm Based on Differential Hamming DistanceDONG Jia-wei, SUN Fu-zhen, WU Xiang-shuai, WU Tian-hui, WANG Shao-qing0School of Computer Science and Technology,Shandong University of Technology,Zibo,Shandong 255000,ChinaCurrent 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.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-12-178.pdfhamming distance|differential hamming distance|bit weights|recommendation algorithm|variational autoencoder |
| spellingShingle | DONG Jia-wei, SUN Fu-zhen, WU Xiang-shuai, WU Tian-hui, WANG Shao-qing Variational Recommendation Algorithm Based on Differential Hamming Distance Jisuanji kexue hamming distance|differential hamming distance|bit weights|recommendation algorithm|variational autoencoder |
| title | Variational Recommendation Algorithm Based on Differential Hamming Distance |
| title_full | Variational Recommendation Algorithm Based on Differential Hamming Distance |
| title_fullStr | Variational Recommendation Algorithm Based on Differential Hamming Distance |
| title_full_unstemmed | Variational Recommendation Algorithm Based on Differential Hamming Distance |
| title_short | Variational Recommendation Algorithm Based on Differential Hamming Distance |
| title_sort | variational recommendation algorithm based on differential hamming distance |
| topic | hamming distance|differential hamming distance|bit weights|recommendation algorithm|variational autoencoder |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-12-178.pdf |
| work_keys_str_mv | AT dongjiaweisunfuzhenwuxiangshuaiwutianhuiwangshaoqing variationalrecommendationalgorithmbasedondifferentialhammingdistance |