A Time-Aware CNN-Based Personalized Recommender System
Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of m...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9476981 |
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| _version_ | 1832564847987916800 |
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| author | Dan Yang Jing Zhang Sifeng Wang XueDong Zhang |
| author_facet | Dan Yang Jing Zhang Sifeng Wang XueDong Zhang |
| author_sort | Dan Yang |
| collection | DOAJ |
| description | Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation. |
| format | Article |
| id | doaj-art-b583dd615cc1492db038e9e434547581 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b583dd615cc1492db038e9e4345475812025-02-03T01:10:02ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/94769819476981A Time-Aware CNN-Based Personalized Recommender SystemDan Yang0Jing Zhang1Sifeng Wang2XueDong Zhang3School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, Liaoning 114051, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, Liaoning 114051, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, Shandong 276826, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, Liaoning 114051, ChinaRecommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.http://dx.doi.org/10.1155/2019/9476981 |
| spellingShingle | Dan Yang Jing Zhang Sifeng Wang XueDong Zhang A Time-Aware CNN-Based Personalized Recommender System Complexity |
| title | A Time-Aware CNN-Based Personalized Recommender System |
| title_full | A Time-Aware CNN-Based Personalized Recommender System |
| title_fullStr | A Time-Aware CNN-Based Personalized Recommender System |
| title_full_unstemmed | A Time-Aware CNN-Based Personalized Recommender System |
| title_short | A Time-Aware CNN-Based Personalized Recommender System |
| title_sort | time aware cnn based personalized recommender system |
| url | http://dx.doi.org/10.1155/2019/9476981 |
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