基于双边兴趣的社交网好友推荐方法研究

Recommendation algorithm of SNS friends based on bilateral interest

随着社交网的广泛流行,用户的数量也急剧增加,针对社交网络用户难以在海量用户环境中快速发现其可能感兴趣的潜在好友的问题,各种推荐算法应运而生,协同过滤算法便是其中最为成功的思想。然而目前的协同过滤算法普遍存在数据稀疏性和推荐精度低等问题,为此提出一种基于动态K-means聚类双边兴趣协同过滤好友推荐算法。该算法结合动态K-means算法对用户进行聚类以降低稀疏性,同时提出相似度可信值的概念调整相似度计算方法以提高相似度精度;利用调整后的相似度分别从用户的吸引与偏好两方面计算近邻用户集,综合考虑这两方面近邻对当前用户的择友影响来生成推荐列表。实验证明,相较于基于用户的协同过滤算法,该算法能有效提高系统的推荐精度与效率。

The rapidly development of SNS makes the dramatic increasement in the number of users. To deal with the difficulty for users to find potential friends quickly, many personalized recommendation technologies emerge. Among them collaborative filtering algorithm is most popular. However, collaborative filtering algorithm also has some drawbacks, like sparsity and low accuracy. In this paper, a bidirectional collaborative filtering algorithm combined with dynamic K-means clustering algorithm is proposed. It applies dynamic K-means clustering algorithm to cluster the similar users into the same cluster, and proposes the concept of similarity credible value to improve the accuracy of similarity. Then it exploits the improved similarity method to compute the neighbors respectively from the taste aspect and attract aspect. It generates the recommendation list based on both the suggestion from taste and attract neighbors. Experimental result demonstrates that this algorithm turns out to be superior to traditional CF algorithm in accuracy of recommendation.