《計算機應用研究》|Application Research of Computers

耦合輔助信息的矩陣分解推薦模型

Matrix factorization recommendation model based on side information

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作者 蔣偉,秦志光
機構 電子科技大學 信息與軟件工程學院,成都 610054
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文章編號 1001-3695(2019)10-004-2900-07
DOI 10.19734/j.issn.1001-3695.2018.03.0206
摘要 近十年來,協同過濾(CF)推薦系統成功地為用戶提供了個性化的產品和服務。然而,用戶—物品矩陣的稀疏性、推薦精度不高等問題仍然是一個挑戰。針對這些問題,在矩陣分解模型基礎上,提出了耦合用戶和物品輔助信息的矩陣分解混合協同過濾框架;然后,基于此框架又提出了耦合物品屬性信息相似度(COS)的過濾模型。大規模真實數據集上的實驗表明,該模型不但可以有效解決物品相似度度量問題,而且相比傳統方法,尤其是在物品特征非常稀疏的情況下,推薦準確性得到了有效改進。
關鍵詞 推薦系統; 混合協同過濾; 矩陣分解; 物品相似度; 耦合對象相似度; 輔助信息
基金項目 四川省科技計劃資助項目(2015JY0178,2014GZ0109,2015KZ002,2015JY0030)
國家自然科學基金資助項目(61472064)
中央高校基本科研基金資助項目(ZYGX2014J051,ZYGX2014J066)
本文URL http://www.pbxovf.icu/article/01-2019-10-004.html
英文標題 Matrix factorization recommendation model based on side information
作者英文名 Jiang Wei, Qin Zhiguang
機構英文名 School of Information & Software Engineering,University of Electronic Science & Technology of China,Chengdu 610054,China
英文摘要 Collaborative filtering(CF) recommender systems have been used to provide users with personalized products and services successfully in the past decade. However, sparseness of user-item matrix and the low accuracy are still challenges. To solve these problems, this paper proposed an ensemble framework based on matrix factorization CF for integrating side information of users and items. Based on this framework, this paper proposed a hybrid CF model for integrating COS(coupled object similarity) of attribute information of items. Extensive experiments conducted over large-scale real-word datasets demonstrate that the proposed approach can effectively solve the problem of item similarity measurement. And compared with the traditional approaches, especially in the case of very sparse feature, the accuracy of the recommendation is improved effectively.
英文關鍵詞 recommender system; hybrid collaborative filtering; matrix factorization; item similarity; coupled object similarity; side information
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收稿日期 2018/3/27
修回日期 2018/5/11
頁碼 2900-2906
中圖分類號 TP181
文獻標志碼 A
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