《计算机应用研究》|Application Research of Computers

嵌入项目疲劳和多样偏好的聚合推荐算法

Aggregation recommendation algorithm for embedding item fatigue and diversity preference

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作者 阙正昊,邓明通,刘学军,李斌
机构 南京工业大学 计算机科学与技术学院,南京 211816
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文章编号 1001-3695(2019)11-003-3220-04
DOI 10.19734/j.issn.1001-3695.2018.04.0279
摘要 为了解决推荐列表偏向于热门项目,多样性差的问题,提出了ARIFDP算法(aggregation recommendation algorithm for embedding item fatigue and diversity preference)。首先通过对用户历史反馈数据分析用户的多样性偏好,得出用户的多样倾向度,进而构造了与评价次数负相关的项目疲劳函数,最终将矩阵分解与项目疲劳函数相聚合,并加入多样倾向度调节项目疲劳函数所占权重,增加了冷门项目被推荐的概率。实验结果表明,ARIFDP算法能在保证准确率的前提下有效提高推荐结果的多样性。
关键词 主题模型; 矩阵分解; 多样倾向度; 项目疲劳函数; 推荐多样性
基金项目 江苏省重点研发计划(社会发展)资助项目(BE2015697)
国家自然科学基金资助项目(61203072)
本文URL http://www.arocmag.com/article/01-2019-11-003.html
英文标题 Aggregation recommendation algorithm for embedding item fatigue and diversity preference
作者英文名 Que Zhenghao, Deng Mingtong, Liu Xuejun, Li Bin
机构英文名 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.
英文关键词 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
页码 3220-3223,3257
中图分类号 TP301.6
文献标志码 A