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

關聯性動態加權的協同過濾推薦

Relevance dynamic weighted collaborative filtering recommendation

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作者 王劍,余青松
機構 華東師范大學 計算機科學與軟件工程學院,上海 200333
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文章編號 1001-3695(2019)11-005-3230-03
DOI 10.19734/j.issn.1001-3695.2018.05.0306
摘要 利用傳統的協同過濾(CF)算法進行推薦時,由于用戶評分矩陣比較稀疏,直接得到的用戶或者項目之間的相似度相對而言可信度就比較低。為了解決這個問題,在傳統的協同過濾基礎上,引入項目與項目之間的關聯性,通過在項目的類別標簽和二部圖的方法之間構建動態權重因子來融合這兩種關聯,形成非對等關聯性關系,并做出用戶對項目的評分預測,從而解決評分矩陣過于稀疏的問題。研究結果表明,相比于傳統方法中使用對等相似度關系以及固定權值的方法,通過動態權重融合關聯性形成非對等的關系的方法,更貼合生活實際,并且有更好的推薦效果。
關鍵詞 協同過濾; 評分矩陣; 稀疏; 動態權重因子; 非對等關聯性
基金項目
本文URL http://www.ziusle.tw/article/01-2019-11-005.html
英文標題 Relevance dynamic weighted collaborative filtering recommendation
作者英文名 Wang Jian, Yu Qingsong
機構英文名 College of Computer Science & Software Engineering,East China Normal University,Shanghai 200333,China
英文摘要 When the traditional collaborative filtering algorithm is used for recommendation, the credibility based on similarity between users or items directly obtained is relatively low due to the sparsity of the user rating matrix. In order to solve this problem, this paper introduced the relevance between projects on the basis of traditional collaborative filtering. It established the association in a non-reciprocal condition by building a dynamic weighting factor between the project's category label and bipartite graph approach, and the user's rating of the project would be predicted. As a result, compared with the method of using the equivalent similarity and the fixed weight value in the traditional method, the method of using non-equivalent relationship formed by the dynamic weights is more in line with the reality of life and has a better recommendation effect.
英文關鍵詞 collaborative filtering; rating matrix; sparse; dynamic weighting factor; non-equivalent relationship
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收稿日期 2018/5/25
修回日期 2018/7/9
頁碼 3230-3232,3249
中圖分類號 TP301.6
文獻標志碼 A
中超外援名额