作　　者： (曾安）; (徐小强）;

机构地区： 广东工业大学计算机学院,广州510006

出　　处： 《计算机科学》 2017年第8期246-251,共6页

摘　　要： 针对传统推荐算法存在数据稀疏影响推荐效果的问题,考虑到社交网络中的链路预测能够综合考虑用户节点之间的拓扑结构,以及好友关系能反映用户的兴趣爱好,提出了一种融合好友关系和标签信息的推荐算法。首先,借助网络资源分配算法对社交网络的结构信息进行特征提取;然后,利用TF-IDF构建合理的社会化标签模型;最后,利用线性模型融合两方面的信息,从而实现推荐。在Last.fm和Delicious数据集上的实验表明,与传统算法相比,所提算法在推荐的召回率和准确率指标上有显著提高。 The recommendation preference of a recommendation system was greatly affected by data sparseness.In order to solve this problem,a hybird collaborative filtering recommendation algorithm based on social network and tag information was proposed in this paper.The topology similarity characteristics among user nodes can be incorporated in the prediction of links in social network,and a circle of friends can exhibit a user＇s interests.Thus,network resource allocation algorithm is firstly utilized to extract social network structure information.Then,the tag information is resonably extracted with the help of TF-IDF method.Finally,the recommendation is made by linearly combining both the social network structure information and the tag information.The experiment results on Last.fm and Delicious dataset suggest that the advocated alogrithm is superior to other advanced approaches in both accuracy and reliability.

关 键 词： 链路预测 社交关系 标签 推荐算法