A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning
In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In or...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/6480273 |
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| _version_ | 1849686109355769856 |
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| author | Xibin Wang Zhenyu Dai Hui Li Jianfeng Yang |
| author_facet | Xibin Wang Zhenyu Dai Hui Li Jianfeng Yang |
| author_sort | Xibin Wang |
| collection | DOAJ |
| description | In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency. |
| format | Article |
| id | doaj-art-b87fbee17fb34c2180ec74d5c2a447e4 |
| institution | DOAJ |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-b87fbee17fb34c2180ec74d5c2a447e42025-08-20T03:22:49ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/64802736480273A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active LearningXibin Wang0Zhenyu Dai1Hui Li2Jianfeng Yang3School of Data Science, Guizhou Institute of Technology, Guiyang 550003, Guizhou, ChinaCollege of Computer Science & Technology, Guizhou University, Guiyang 550025, Guizhou, ChinaCollege of Computer Science & Technology, Guizhou University, Guiyang 550025, Guizhou, ChinaSchool of Data Science, Guizhou Institute of Technology, Guiyang 550003, Guizhou, ChinaIn the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.http://dx.doi.org/10.1155/2020/6480273 |
| spellingShingle | Xibin Wang Zhenyu Dai Hui Li Jianfeng Yang A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning Discrete Dynamics in Nature and Society |
| title | A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning |
| title_full | A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning |
| title_fullStr | A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning |
| title_full_unstemmed | A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning |
| title_short | A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning |
| title_sort | new collaborative filtering recommendation method based on transductive svm and active learning |
| url | http://dx.doi.org/10.1155/2020/6480273 |
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