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

嵌入項目疲勞和多樣偏好的聚合推薦算法

Aggregation recommendation algorithm for embedding item fatigue and diversity preference

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作者 闕正昊,鄧明通,劉學軍,李斌
機構 南京工業大學 計算機科學與技術學院,南京 211816
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文章編號 1001-3695(2019)11-003-3220-04
DOI 10.19734/j.issn.1001-3695.2018.04.0279
摘要 為了解決推薦列表偏向于熱門項目,多樣性差的問題,提出了ARIFDP算法(aggregation recommendation algorithm for embedding item fatigue and diversity preference)。首先通過對用戶歷史反饋數據分析用戶的多樣性偏好,得出用戶的多樣傾向度,進而構造了與評價次數負相關的項目疲勞函數,最終將矩陣分解與項目疲勞函數相聚合,并加入多樣傾向度調節項目疲勞函數所占權重,增加了冷門項目被推薦的概率。實驗結果表明,ARIFDP算法能在保證準確率的前提下有效提高推薦結果的多樣性。
關鍵詞 主題模型; 矩陣分解; 多樣傾向度; 項目疲勞函數; 推薦多樣性
基金項目 江蘇省重點研發計劃(社會發展)資助項目(BE2015697)
國家自然科學基金資助項目(61203072)
本文URL http://www.ziusle.tw/article/01-2019-11-003.html
英文標題 Aggregation recommendation algorithm for embedding item fatigue and diversity preference
作者英文名 Que Zhenghao, Deng Mingtong, Liu Xuejun, Li Bin
機構英文名 College of Computer Science & Technology,Nanjing Tech University,Nanjing 211816,China
英文摘要 In order to solve the problem that the recommendation list is biased towards popular projects and with poor diversity, this paper proposed the ARIFDP(aggregation recommendation algorithm for embedding item fatigue and diversity prefe-rence) algorithm. First, it analyzed the user's diversity preferences by using the historical feedback data of the user to derive the user, got the degree of diversification. And then it constructed a project fatigue function negatively related to the number of evaluations; eventually aggregated the matrix decomposition and the project fatigue function, and the inclusion of various propensity degrees adjusted the weight of the fatigue function of the project, and increased the probability of the proposed project. Experimental results show that the ARIFDP algorithm can effectively improve the diversity of recommendation results on the premise of ensuring accuracy.
英文關鍵詞 topic model; matrix factorization; diversity tendency; item fatigue function; recommendation diversity
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收稿日期 2018/4/9
修回日期 2018/6/11
頁碼 3220-3223,3257
中圖分類號 TP301.6
文獻標志碼 A
中超外援名额