Dynamic User Modeling in Social Media Systems

doi:10.1145/2699670

CORC > 北京大学 > 信息科学技术学院

	Dynamic User Modeling in Social Media Systems
	Yin, Hongzhi ; Cui, Bin ; Chen, Ling ; Hu, Zhiting ; Zhou, Xiaofang
刊名	ACM TRANSACTIONS ON INFORMATION SYSTEMS
	2015
关键词	Algorithms Design Experimentation Performance
DOI	10.1145/2699670
英文摘要	Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests. To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top-k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.; Australian Research Council [DP110103423, DP120102829]; National Natural Science Foundation of China [61272155]; 973 program [2014CB340405]; ARC Discovery Project [DP140100545]; SCI(E); ARTICLE; bin.cui@pku.edu.cn; 3; 33
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/420721]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yin, Hongzhi,Cui, Bin,Chen, Ling,et al. Dynamic User Modeling in Social Media Systems[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2015.
APA	Yin, Hongzhi,Cui, Bin,Chen, Ling,Hu, Zhiting,&Zhou, Xiaofang.(2015).Dynamic User Modeling in Social Media Systems.ACM TRANSACTIONS ON INFORMATION SYSTEMS.
MLA	Yin, Hongzhi,et al."Dynamic User Modeling in Social Media Systems".ACM TRANSACTIONS ON INFORMATION SYSTEMS (2015).