An Efficient Hybrid Recommendation Model With Deep Neural Networks
Recently, deep learning has gained great popularity in the area of recommender systems. Various combinations of deep learning, collaborative recommendation and content-based recommendation have occurred. However, as one of the three most significant recommendation techniques, hybrid recommendation h...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8766091/ |
| _version_ | 1828885067941806080 |
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| author | Zhenhua Huang Chang Yu Juan Ni Hai Liu Chun Zeng Yong Tang |
| author_facet | Zhenhua Huang Chang Yu Juan Ni Hai Liu Chun Zeng Yong Tang |
| author_sort | Zhenhua Huang |
| collection | DOAJ |
| description | Recently, deep learning has gained great popularity in the area of recommender systems. Various combinations of deep learning, collaborative recommendation and content-based recommendation have occurred. However, as one of the three most significant recommendation techniques, hybrid recommendation has little cooperation with deep learning. Besides, most current deep hybrid models only incorporate two simple recommendation methods together in post-fusion, leaving massive space for further exploration of better combinations. In this paper, we apply deep learning to hybrid recommendation, proposing a deep hybrid recommendation model DMFL (Deep Metric Factorization Learning). In DMFL, we combine deep learning with improved machine learning models to learn the interaction between users and items from multiple perspectives. Such deep hybrid learning helps to reflect the user preference more comprehensively and strengthen model's ability of generalization. We also propose a more accurate method of user feature representation, taking both long-term static characteristics and short-term dynamic interest changes of users into consideration. Furthermore, thorough experiments have been conducted on real-world datasets, which strongly proves the effectiveness and efficiency of the proposed model. |
| first_indexed | 2024-12-13T11:18:38Z |
| format | Article |
| id | doaj.art-6a9f3eaaaeb846adb633e6838f696ab3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:18:38Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6a9f3eaaaeb846adb633e6838f696ab32022-12-21T23:48:32ZengIEEEIEEE Access2169-35362019-01-01713790013791210.1109/ACCESS.2019.29297898766091An Efficient Hybrid Recommendation Model With Deep Neural NetworksZhenhua Huang0https://orcid.org/0000-0002-5491-1120Chang Yu1Juan Ni2Hai Liu3Chun Zeng4Yong Tang5School of Computer Science, South China Normal University, Guangzhou, ChinaDepartment of Computer Science, Tongji University, Shanghai, ChinaSchool of Politics and Administration, South China Normal University, Guangzhou, ChinaSchool of Computer Science, South China Normal University, Guangzhou, ChinaSchool of Computer Science, South China Normal University, Guangzhou, ChinaSchool of Computer Science, South China Normal University, Guangzhou, ChinaRecently, deep learning has gained great popularity in the area of recommender systems. Various combinations of deep learning, collaborative recommendation and content-based recommendation have occurred. However, as one of the three most significant recommendation techniques, hybrid recommendation has little cooperation with deep learning. Besides, most current deep hybrid models only incorporate two simple recommendation methods together in post-fusion, leaving massive space for further exploration of better combinations. In this paper, we apply deep learning to hybrid recommendation, proposing a deep hybrid recommendation model DMFL (Deep Metric Factorization Learning). In DMFL, we combine deep learning with improved machine learning models to learn the interaction between users and items from multiple perspectives. Such deep hybrid learning helps to reflect the user preference more comprehensively and strengthen model's ability of generalization. We also propose a more accurate method of user feature representation, taking both long-term static characteristics and short-term dynamic interest changes of users into consideration. Furthermore, thorough experiments have been conducted on real-world datasets, which strongly proves the effectiveness and efficiency of the proposed model.https://ieeexplore.ieee.org/document/8766091/Recommender systemdeep learninghybrid recommendationcomparative learningperformance evaluation |
| spellingShingle | Zhenhua Huang Chang Yu Juan Ni Hai Liu Chun Zeng Yong Tang An Efficient Hybrid Recommendation Model With Deep Neural Networks IEEE Access Recommender system deep learning hybrid recommendation comparative learning performance evaluation |
| title | An Efficient Hybrid Recommendation Model With Deep Neural Networks |
| title_full | An Efficient Hybrid Recommendation Model With Deep Neural Networks |
| title_fullStr | An Efficient Hybrid Recommendation Model With Deep Neural Networks |
| title_full_unstemmed | An Efficient Hybrid Recommendation Model With Deep Neural Networks |
| title_short | An Efficient Hybrid Recommendation Model With Deep Neural Networks |
| title_sort | efficient hybrid recommendation model with deep neural networks |
| topic | Recommender system deep learning hybrid recommendation comparative learning performance evaluation |
| url | https://ieeexplore.ieee.org/document/8766091/ |
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