基于用户标注行为的潜在好友推荐

目前多数社交网络主要根据已有好友关系推荐潜在好友,用户的兴趣爱好不作为主要考虑因素;此外,如何从大量数据中精确地提取用户的兴趣爱好是一项十分艰巨的任务。为此,提出一种在大量标注行为数据中精确挖掘出用户的兴趣爱好,并据此推荐具有相同兴趣爱好的潜在好友的算法--基于标注的好友推荐(FRBT)算法。首先使用词频-逆向文件频率(TF-IDF)对标签进行聚类,将语义相似的标签聚成话题;然后在话题的基础上提出一种新的相似度公式来计算用户相似度;再融合基于话题与基于物品的用户相似度,将相似度较高的用户作为潜在好友进行推荐。在Delicious数据集上以准确率和召回率为指标与item、tag和tri-graph三种算法进行比较,实验验证了该算法能够更准确地为用户推荐兴趣相似的好友。

Potential friend recommendation based on user tagging

At present, most social networking systems recommend potential friends mainly according to the existed friend relationship, and users' interests are not emphasized. Furthermore, it is a very difficult task to find users' interests with high precision from a large amount of data. A Friend Recommendation Based on user Tagging (FRBT) algorithm was proposed to find potential friends with the same interests by mining users' interests in tagging behavior data. First, Term Frequency-Inverse Document Frequency (TF-IDF) was used to cluster the similar semantic tags into topics. A new formula for calculating the users' similarity of topics was described. Combined with the user similarity based on topic and item, the proposed algorithm could recommend the users with high similarities as potential friends. The experimental results on tagging dataset of Delicious validate, compared wtih the algorithms of item, tag and tri-graph, FRBT has better performance in terms of precision and recall.