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

基于多元關系的張量分解標簽推薦方法

Method for tag recommendation of tensor decomposition based on multiple relationships

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作者 曾輝,胡強,淦修修
機構 華東交通大學 信息工程學院,南昌 330013
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文章編號 1001-3695(2019)10-005-2907-04
DOI 10.19734/j.issn.1001-3695.2018.04.0215
摘要 標簽推薦的現有方法忽視了多種屬性特征之間的聯系,無法保證大數據環境下推薦系統的準確率。針對該問題,提出了一種基于用戶聚類和張量分解的新標簽推薦方法,以進一步提高標簽推薦的質量。該方法首先對一些對產品具有重要影響的用戶進行聚類,然后根據用戶、產品、標簽和產品評分之間的多元關系綜合計算總權重。最后,根據聚類之后的用戶群體以及多元關系的總權值構建張量并進行張量因式分解。實驗與傳統張量分解方法相對比,結果表明提出的方法在準確率上具有一定的提高,驗證了算法的有效性。
關鍵詞 標簽推薦; 張量因子分解; 權重; 聚類
基金項目 國家自然科學基金資助項目(61562027)
江西省教育廳科學技術研究資助項目(GJJ170379)
本文URL http://www.pbxovf.icu/article/01-2019-10-005.html
英文標題 Method for tag recommendation of tensor decomposition based on multiple relationships
作者英文名 Zeng Hui, Hu Qiang, Gan Xiuxiu
機構英文名 College of Information Engineering,East China Jiaotong University,Nanchang 330013,China
英文摘要 The exist methodd of tag recommendation ignore the connection among the characteristics of a variety of attributes and cannot guarantee the accuracy of the recommender system in the big data environment. Aiming at this problem, this paper proposed a tag recommendation method based on user clustering and tensor decomposition, which could further improve the quality of tag recommendation. The method firstly clustered the users who had an important influence on the product, and then comprehensively calculated the total weight based on the multiple relationships among the users, products, tags, and product ratings. Finally, it constructed the tensor according to the user groups after clustering and the total weight of the multivariate relations, and performed the tensor factorization. Experiment compared with the traditional tensor decomposition method, and the results show that proposed method improves the accuracy and verifies the effectiveness of the algorithm.
英文關鍵詞 tag recommendation; tensor factorization; weight; clustering
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收稿日期 2018/4/3
修回日期 2018/5/14
頁碼 2907-2910
中圖分類號 TP181
文獻標志碼 A
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