کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947461 | 1439578 | 2017 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems
ترجمه فارسی عنوان
اطلاعات ذاتی معدن با استفاده از رویکردهای مبتنی بر فاکتورهای ماتریس برای فیلتر کردن مشارکتی در سیستم های توصیه می شود
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Matrix factorization (MF) is an increasingly important approach in the field of missing value prediction because recommender systems are rapidly becoming ubiquitous. MF-based collaborative filtering (CF) seeks to improve recommender performance by combining user-item matrix with MF. However, most MF-based approaches available at present could not obtain high prediction accuracy because of the sparse availability of user-item matrices in CF models. The present paper proposes a framework that involves two efficient MF, dynamic single-element-based CF-integrating manifold regularization (DSMMF) and dynamic single-element-based Tikhonov graph regularization non-negative MF (DSTNMF). The aim of this framework is to better use the intrinsic structure of user-item rating matrix and user/item content information, overcome the dimensionality curse and ill-posed problem of weighted graph NMF, and evade the frequent manipulations of indicator matrices that lack practicability. We validate the effectiveness of our proposed algorithms with respect to recommender performance by four indices on three datasets. We demonstrate that our proposed approaches lead to considerable improvement compared with several other state-of-the-art approaches.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 249, 2 August 2017, Pages 48-63
Journal: Neurocomputing - Volume 249, 2 August 2017, Pages 48-63
نویسندگان
Li Yangyang, Wang Dong, He Haiyang, Jiao Licheng, Xue Yu,