A temporal context-aware model for user behavior modeling in social media systems

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Huang, Zi (2014). A temporal context-aware model for user behavior modeling in social media systems. In: SIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, Snowbird, UT United States, (1543-1554). 22-27 June 2014. doi:10.1145/2588555.2593685

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Yin, Hongzhi
Cui, Bin
Chen, Ling
Hu, Zhiting
Huang, Zi
Title of paper A temporal context-aware model for user behavior modeling in social media systems
Conference name 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014
Conference location Snowbird, UT United States
Conference dates 22-27 June 2014
Convener Curtis Dyreson
Proceedings title SIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data   Check publisher's open access policy
Journal name Association for Computing Machinery. Special Interest Group on Management of Data. International Conference Proceedings   Check publisher's open access policy
Series Association for Computing Machinery. Special Interest Group on Management of Data. International Conference Proceedings
Place of Publication New York, NY United States
Publisher Association for Computing Machinery
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1145/2588555.2593685
Open Access Status Not yet assessed
ISBN 9781450323765
ISSN 0730-8078
Start page 1543
End page 1554
Total pages 12
Language eng
Abstract/Summary Social media provides valuable resources to analyze user behaviors and capture user preferences. This paper 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. To further improve the performance of TCAM, an item-weighting scheme is proposed to enable TCAM to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on TCAM, we design an efficient query processing technique to support fast online recommendation for large social media data. Extensive experiments have been conducted to evaluate the performance of TCAM on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of the TCAM models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.
Keyword Probabilistic generative model
Social media mining
Temporal recommender system
User behavior modeling
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 54 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 05 Aug 2014, 13:29:47 EST by System User on behalf of School of Information Technol and Elec Engineering