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

融合项目偏差与用户偏好的推荐算法

Recommendation algorithm combining item deviation and user preference

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作者 程磊,高茂庭
机构 上海海事大学 信息工程学院,上海 201306
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2019)11-006-3233-04
DOI 10.19734/j.issn.1001-3695.2018.05.0298
摘要 针对协同过滤推荐中由于项目和用户间关联因素的相互影响而存在项目偏差和用户偏好的问题,提出一种融合项目偏差与用户偏好的推荐算法。先进行聚类处理,包括LDA主题建模生成项目簇和K-means聚类生成用户簇;再依次根据项目簇和用户簇的约束生成项目偏差分,同时以用户项目评分及项目类型为基础,经过概率转移得到用户偏好分;最后以项目簇内已有评分的均值为基础,对项目偏差分和用户偏好分进行线性加权生成预测评分。对比实验表明,新算法能够根据不同的近邻得到合理的推荐,提高推荐的准确度。
关键词 协同过滤; 主题建模; 聚类; 项目偏差; 用户偏好
基金项目 国家自然科学基金资助项目(61202022)
上海海事大学研究生创新基金资助项目(2017ycx061)
本文URL http://www.arocmag.com/article/01-2019-11-006.html
英文标题 Recommendation algorithm combining item deviation and user preference
作者英文名 Cheng Lei, Gao Maoting
机构英文名 College of Information Engineering,Shanghai Maritime University,Shanghai 201306,China
英文摘要 Aiming at the problem that there are item deviation and user preferences in collaborative filtering recommendation for the interaction between factors related in items and users, this paper proposed a recommendation algorithm integrated item deviation and user preference. Firstly it clustered to generate item clusters on LDA topics modeling and to get user clusters by using K-means; then it generated item deviation score on the constraints of item cluster and user cluster, and obtained user preference score with probability transfer on user-item score and item type. Finally it weighted the item deviation score and user preference score linearly to form the prediction score based on the existing scoring average in the item cluster. Comparison experiments show that the new algorithm can obtain reasonable recommendation based on different neighbors and improve reco-mmendation accuracy.
英文关键词 collaborative filtering; topic modeling; clustering; item deviation; user preference
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收稿日期 2018/5/10
修回日期 2018/6/15
页码 3233-3236,3273
中图分类号 TP301.6
文献标志码 A