Collaborative Additional Variational Autoencoder for Top-N Recommender Systems
Collaborative filtering (CF) has been generally used in recommender systems when faced some practical problems. Due to the sparsity of the rating matrix, the traditional CF-based approach has a significant decline in recommendation performance. Gradually, a hybrid method, using side information and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8598869/ |
| _version_ | 1818854533234688000 |
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| author | Ming He Qian Meng Shaozong Zhang |
| author_facet | Ming He Qian Meng Shaozong Zhang |
| author_sort | Ming He |
| collection | DOAJ |
| description | Collaborative filtering (CF) has been generally used in recommender systems when faced some practical problems. Due to the sparsity of the rating matrix, the traditional CF-based approach has a significant decline in recommendation performance. Gradually, a hybrid method, using side information and rating information, has been widely employed and achieves great performance. Together with side information and rating information, the hybrid method can overcome the data sparsity and cold-start problems. However, they seem to fail to take into consideration the fact that the sparsity of single side information. To solve this problem, we take full advantage of the characteristics of deep learning that can learn effective representation and propose a novel deep learning model named additional variational autoencoder that considers both content and tag information of the item. The model learns effective latent representations from additional side information, including content information and tag information in an unsupervised manner. With the help of graphical models, it can extract the implicit relationships between users and items effectively. A large number of experimental results on two actual datasets show that our proposed model is superior to other methods, and the performance improvement is achieved. |
| first_indexed | 2024-12-19T07:54:13Z |
| format | Article |
| id | doaj.art-d1963e00268b44b8aebe30e397d824fe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:54:13Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1963e00268b44b8aebe30e397d824fe2022-12-21T20:30:03ZengIEEEIEEE Access2169-35362019-01-0175707571310.1109/ACCESS.2018.28902938598869Collaborative Additional Variational Autoencoder for Top-N Recommender SystemsMing He0Qian Meng1https://orcid.org/0000-0003-3104-6696Shaozong Zhang2https://orcid.org/0000-0001-7322-2841Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaCollaborative filtering (CF) has been generally used in recommender systems when faced some practical problems. Due to the sparsity of the rating matrix, the traditional CF-based approach has a significant decline in recommendation performance. Gradually, a hybrid method, using side information and rating information, has been widely employed and achieves great performance. Together with side information and rating information, the hybrid method can overcome the data sparsity and cold-start problems. However, they seem to fail to take into consideration the fact that the sparsity of single side information. To solve this problem, we take full advantage of the characteristics of deep learning that can learn effective representation and propose a novel deep learning model named additional variational autoencoder that considers both content and tag information of the item. The model learns effective latent representations from additional side information, including content information and tag information in an unsupervised manner. With the help of graphical models, it can extract the implicit relationships between users and items effectively. A large number of experimental results on two actual datasets show that our proposed model is superior to other methods, and the performance improvement is achieved.https://ieeexplore.ieee.org/document/8598869/Collaborative filteringdeep learningvariational autoencoderside information |
| spellingShingle | Ming He Qian Meng Shaozong Zhang Collaborative Additional Variational Autoencoder for Top-N Recommender Systems IEEE Access Collaborative filtering deep learning variational autoencoder side information |
| title | Collaborative Additional Variational Autoencoder for Top-N Recommender Systems |
| title_full | Collaborative Additional Variational Autoencoder for Top-N Recommender Systems |
| title_fullStr | Collaborative Additional Variational Autoencoder for Top-N Recommender Systems |
| title_full_unstemmed | Collaborative Additional Variational Autoencoder for Top-N Recommender Systems |
| title_short | Collaborative Additional Variational Autoencoder for Top-N Recommender Systems |
| title_sort | collaborative additional variational autoencoder for top n recommender systems |
| topic | Collaborative filtering deep learning variational autoencoder side information |
| url | https://ieeexplore.ieee.org/document/8598869/ |
| work_keys_str_mv | AT minghe collaborativeadditionalvariationalautoencoderfortopnrecommendersystems AT qianmeng collaborativeadditionalvariationalautoencoderfortopnrecommendersystems AT shaozongzhang collaborativeadditionalvariationalautoencoderfortopnrecommendersystems |