Research on User Behavior Understanding and Personalized Service Recommendation Algorithm in Twitter Social Networks
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摘要: 随着社交网迅速发展，产生了大量带有时空信息的短文本数据.这些短文本数据因其文本长度过短且所带地理位置信息过于稀疏导致用户行为主题难于捕捉.此外，由于目前大多数用户行为理解相关研究工作缺少对行为要素间依赖关系的适度融合，因而造成行为理解具有片面性.基于此，首先提出2种综合考虑用户行为发生时间、活动内容、活动区域的用户-时间-活动模型(user-time-activity model, UTAM)和用户-时间-区域模型(user-time-region model, UTRM)，用于深刻理解用户行为规律；然后利用LDA(latent Dirichlet allocation)技术，抽取用户活动-服务主题，提出活动-服务主题模型(activity-to-service topic model, ASTM)，用于挖掘活动和服务间的对应关系；最后将服务地点属性内耦合性纳入考虑，提出了基于耦合和距离的矩阵分解(matrix factorization based on couple & distance, MFCD)算法，用于提高推荐质量.为验证所提模型和算法的有效性，在真实Twitter数据集上进行了扩展性实验，结果表明:所提模型对提高个性化服务推荐质量是有效的，MFCD算法对于用户的行为理解效果也优于传统矩阵分解算法.Abstract: With the rapid development of social networks in recent years, a large amount of short text data with time-spacial information is produced accordingly. Due to short length of text and sparseness of geographic location, it is very difficult to capture the semantic topics of user behavior. In addition, most existing research work related to user behavior understanding has not taken the behavior elements dependency into account, which results in the incomplete understanding of user behavior. Based on these, two models mixed with time, activity and region, i.e., user-time-activity model (UTAM) and user-time-region model (UTRM), are proposed firstly in this paper so as to explore behavior principles effectively. And then, by extracting activity-service topics based on latent Dirichlet allocation (LDA) techniques, an activity-to-service topic model (ASTM) is proposed in order to mine corresponding relationships between activities and services. Finally, a novel matrix factorization algorithm fused with distance and coupled similarity, i.e., matrix factorization based on couple & distance (MFCD), is put forward to improve the recommendation quality. In order to verify the effectiveness of proposed models and algorithms, extensive experiments are executed on a real Twitter dataset. Experimental results show that the proposed models can improve the quality of personalized recommendation service greatly, and the performance of MFCD algorithm is superior to the traditional matrix factorization algorithm on the effect of understanding user behaviors.
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