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A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests
المؤلفون المشاركون
Hua, Qingyi
Chen, Rui
Gao, Quanli
Xing, Ying
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-22، 22ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-03
دولة النشر
مصر
عدد الصفحات
22
التخصصات الرئيسية
الملخص EN
Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items.
In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent.
However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix.
Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation.
To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology.
Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted.
Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity.
Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group.
Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings.
Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models.
Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Rui& Hua, Qingyi& Gao, Quanli& Xing, Ying. 2018. A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-22.
https://search.emarefa.net/detail/BIM-1209556
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Rui…[et al.]. A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests. Mathematical Problems in Engineering No. 2018 (2018), pp.1-22.
https://search.emarefa.net/detail/BIM-1209556
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Rui& Hua, Qingyi& Gao, Quanli& Xing, Ying. A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-22.
https://search.emarefa.net/detail/BIM-1209556
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1209556
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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