Dynamic user modeling in social media systems

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Zhou, Xiaofang (2015) Dynamic user modeling in social media systems. ACM Transactions on Information Systems, 33 3: 10:1-10:44. doi:10.1145/2699670

Author Yin, Hongzhi
Cui, Bin
Chen, Ling
Hu, Zhiting
Zhou, Xiaofang
Title Dynamic user modeling in social media systems
Journal name ACM Transactions on Information Systems   Check publisher's open access policy
ISSN 1046-8188
Publication date 2015-03
Sub-type Article (original research)
DOI 10.1145/2699670
Volume 33
Issue 3
Start page 10:1
End page 10:44
Total pages 44
Place of publication New York, United States
Publisher A C M Special Interest Group
Collection year 2016
Language eng
Formatted abstract
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.
Keyword User behavior modeling
Temporal recommender system
Probabilistic generative model
Social media mining
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article # 10

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 7 times in Scopus Article | Citations
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Created: Sun, 01 Mar 2015, 11:21:15 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering