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工程科学与技术:2019,51(1):189-196
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基于用户兴趣评分填充的改进混合推荐方法
(1.河南大学 计算机与信息工程学院, 河南 开封 475004;2.三峡大学 湖北省水电工程智能视觉监测重点实验室, 湖北 宜昌 443002)
Improved Hybrid Recommendation Approach Based on User Interest Ratings Filling
(1.School of Computer and Info. Eng., Henan Univ., Kaifeng 475004, China;2.Key Lab. of Intelligent Vision Monitoring for Hydropower Project of Hubei Province, Three Gorges Univ., Yichang 443002, China)
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投稿时间:2018-05-16    修订日期:2018-12-06
中文摘要: 针对传统协同过滤推荐方法中的用户项目评分数据稀疏和推荐准确度不高的问题,提出了一种基于用户兴趣评分填充的改进混合推荐方法。首先,分析用户对项目类型的偏好,计算用户兴趣评分并进行矩阵填充;然后,考虑用户主观评分差异化及项目自身质量的影响,对传统皮尔逊相关系数进行改进,并基于已填充评分矩阵进行用户相似性及项目相似性计算;在此基础上分别基于用户和项目两个方面进行评分预测,并将两者的预测评分进行加权求和,进而进行混合推荐;最后,以Movielens100k为数据集进行实验,先分析了用户兴趣评分矩阵的填充效果,再将文中方法和传统协同过滤混合推荐方法以及文献中提出方法进行了对比分析。实验结果表明;提出的评分矩阵填充方法能有效缓解数据稀疏的影响,填充效果优于传统评分矩阵填充方法;提出的改进混合推荐方法(IHRIRF)比传统的混合协同过滤推荐方法HCFR及WPCC方法具有更好地推荐效果。
Abstract:Aiming at the problems of data sparseness of user item ratings and low recommendation accuracy in traditional collaborative filtering recommendation methods, an improved hybrid recommendation approach based on user interest ratings filling was proposed. Firstly, the users' preference to the item types was analyzed, and the user interest ratings were calculated. Afterwards, the operation of matrix filling was performed. Then the impact of users' subjective ratings differentiation and the item's own quality were considered, and the traditional Pearson correlation coefficient was improved. Based on the filled ratings matrix, users' similarity and items' similarity were computed, to predict ratings from the perspective of users and items respectively. Moreover, the weighted sum of two predicted ratings was calculated further to perform the hybrid recommendation. Finally, experiments were carried out on the Movielens100k dataset. The filling effect of user interest ratings matrix was analyzed firstly, and then the proposed approach, traditional collaborative filtering recommendation methods, and previous methods in the literature were compared and analyzed. The results show that the proposed matrix filling method can effectively alleviate the effect of data sparseness, and the filling effect is better than traditional ratings matrix filling methods. Furthermore, our improved hybrid recommendation approach (IHRIRF) has better recommendations than traditional collaborative filtering recommendation method HCRF as well as WPCC method.
文章编号:201800528     中图分类号:TP311    文献标志码:
基金项目:国家重点基础研究发展计划资助项目(2014CB340404);国家自然科学基金资助项目(61402150);中国博士后科学基金资助项目(2016M592286);河南省科技研发专项资助(182102410063);三峡大学水电工程智能视觉监测湖北省重点实验室开放基金资助项目(2016KLA04);河南大学科研基金资助项目(2013YBZR015)
作者简介:李征(1984-),女,副教授,博士.研究方向:Web服务发现与推荐;软件工程.E-mail:lizheng@henu.edu.cn
引用文本:
李征,段垒.基于用户兴趣评分填充的改进混合推荐方法[J].工程科学与技术,2019,51(1):189-196.
LI Zheng,DUAN Lei.Improved Hybrid Recommendation Approach Based on User Interest Ratings Filling[J].Advanced Engineering Sciences,2019,51(1):189-196.