计算机科学与探索2017，Vol.11Issue(10)：1642-1651,10.DOI:10.3778/j.issn.1673-9418.1608002
融合评分倾向度和双重预测的协同过滤推荐算法
Collaborative Filtering Recommendation Method Combining Rating Preference and Dual Prediction
摘要
Abstract
Collaborative filtering recommendation system suffers from series data sparsity problem. To solve the problem, this paper proposes a collaborative filtering recommendation method by combining rating preference and dual prediction. In the stage of calculating the nearest neighbors, to improve the calculation method of similarity, rating preference is introduced firstly. Then, in the stage of generating recommendation, a dual prediction method is pro-posed which is based on the user and the item nearest neighbors to predict the user preference more accurately. The experimental results on the MovieLens-1M data set indicate that the proposed method can relieve the influence of rating data sparsity on recommended results, significantly reduce the mean absolute error and effectively improve the recommendation precision.关键词
推荐系统/协同过滤/用户偏好/评分预测Key words
recommendation system/collaborative filtering/user preference/rating prediction分类
信息技术与安全科学引用本文复制引用
孙萍,李锵,关欣,吕杰..融合评分倾向度和双重预测的协同过滤推荐算法[J].计算机科学与探索,2017,11(10):1642-1651,10.基金项目
The National Natural Science Foundation of China under Grant No. 61401307 (国家自然科学基金) （国家自然科学基金）
the Postdoctoral Science Foun- dation of China under Grant No. 2014M561184 (中国博士后科学基金) （中国博士后科学基金）
the Application Infrastructure and Cutting-Edge Technology Research Projects of Tianjin under Grant No. 15JCYBJC17100 (天津市应用基础与尖端技术研究项目). （天津市应用基础与尖端技术研究项目）
