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A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression
المؤلفون المشاركون
Yu, Xu
Lin, Jun-yu
Jiang, Feng
Du, Jun-wei
Han, Ji-zhong
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-02-12
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains.
Obviously, different auxiliary domains have different importance to the target domain.
However, previous works cannot evaluate effectively the significance of different auxiliary domains.
To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR).
We first construct features in different domains and use these features to represent different auxiliary domains.
Thus the weight computation across different domains can be converted as the weight computation across different features.
Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem.
Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem.
As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods.
We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yu, Xu& Lin, Jun-yu& Jiang, Feng& Du, Jun-wei& Han, Ji-zhong. 2018. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130590
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yu, Xu…[et al.]. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1130590
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yu, Xu& Lin, Jun-yu& Jiang, Feng& Du, Jun-wei& Han, Ji-zhong. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130590
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130590
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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