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A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning
المؤلفون المشاركون
Yang, Jianfeng
Wang, Xibin
Dai, Zhenyu
Li, Hui
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-02
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xibin& Dai, Zhenyu& Li, Hui& Yang, Jianfeng. 2020. A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1153286
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xibin…[et al.]. A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1153286
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xibin& Dai, Zhenyu& Li, Hui& Yang, Jianfeng. A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1153286
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1153286
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
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