基于用户人口统计与专家信任的协同过滤算法

Collaborative filtering algorithm based on user demographics and expert opinions

推荐系统是学术研究的热门课题,在工业界应用也越来越广泛,推荐系统旨在为用户推荐相关的感兴趣的物品。协同过滤算法被用来比较用户及物品的相似度,向用户推荐与其最近邻用户的偏好。为了提高协同过滤算法预测的准确率,提出基于用户人口统计与专家信任的协同过滤算法,先比较用户人口统计属性,然后进一步比较用户与专家的人口统计属性来得到用户与专家的相似度,从而提高预测的准确性。实验验证表明,提出的算法能够有效提高协同过滤算法预测的准确率。

Recommender systems have been used tremendously in academia and industry,and the recommendations generated by these systems aim to offer relevant interesting items to users.Collaborative filtering algorithm is used to calculate the similarities between users and items,and recommends the nearest neighbors＇ preferences to users.In order to improve the prediction accuracy of collaborative filtering algorithm,we propose a collaborative filtering algorithm based on user demographics and expert opinions.First we compare users＇ demographic attributes,which are then compared with expert demographic attributes to calculate the similarities between users and experts.Experimental results verify that the algorithm proposed in this paper can effectively improve the prediction accuracy of collaborative filtering algorithm.