一种结合用户评分信息的改进好友推荐算法  

Improved Friends Recommendation Algorithm Combining with User Rating Information

作　　者：汤颖[1] 钟南江 范菁[1] 

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023

出　　处：《计算机科学》2016年第9期111-115,共5页Computer Science

基　　金：国家自然科学基金(61003265);浙江省自然科学基金(LY14F020021);国家科技支撑计划(2014BAH23F03)资助

摘　　要：传统的好友推荐算法在计算好友相似度时通常仅仅考虑用户在社交网络的拓扑结构的相似性,而对用户的兴趣相似性考虑较少,因此推荐的结果往往不够精准。现有的很多社交网站(如豆瓣网)提供了用户评分功能,用户可以对某类物品(如电影)给出自己的评分。为了在推荐时计算用户的兴趣相似度,提出基于用户给出的对某类物品的评分来计算用户的兴趣相似度,从而在拓扑相似度的基础上结合兴趣相似度得到更精准的推荐结果。首先使用余弦相似度计算出用户间拓扑相似度;其次在计算基于评分的用户兴趣相似度时,通过建立概率模型得到用户聚类评分相似度矩阵,从该评分矩阵推导出用户间基于评分的兴趣相似度;最后,结合拓扑相似度和评分相似度得到最终的改进好友推荐算法,计算出相似度值最高的N个人推荐给当前用户。为了验证所提方法的有效性,用提出的方法对豆瓣网抓取的用户数据进行好友推荐,实验结果证明所提方法与传统的基于拓扑的好友推荐算法相比可以有效提高好友推荐的准确性。Traditional friends recommendation algorithms only consider the topological similarRy m calculating me simi- larity of friends. The similarity of users＇ interests is seldomly taken into account, and the recommendation results are often not precise enough. Many existing social networking sites （such as Douhan. corn） provide the functions of user ratings,i, e. users can give ratings for certain types of items （such as movies）. In order to calculate user＇s interests similarity,we proposed a method which computes the interests similarity based on the ratings given by users and got more accurate result of recommendation by incorporating the interests similarity into the topological similarity. Firstly, we used cosine similarity to calculate the topological similarity between users. In calculating the interests similarity based on user ratings, we got the users cluster rating similarity matrix through the establishment of a probabilistic model, and derived users＇ interests similarity from the rating similarity matrix. Finally, users＇ interests similarity and topologi- cal similarity were combined to get the final improved friends recommendation algorithm. In order to verify the effec- tiveness of our method, we applied our method to the crawled user data from Douban website. The experimental results show that our method can effectively improve the accuracy of recommendation results compared with the traditional recom- mendation algorithm based on topology similarity.

关 键 词：社交网络 推荐 拓扑结构 评分 聚类 相似度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]