A Theme-Context Mixture Model for Personalized Search in Social Network
- 10.2991/meic-15.2015.49How to use a DOI?
- Mixture Mode; Contextual Mining; LDA; PLSA; User Preference Information
Nowadays, social network technology provided a lot of ways for users to express their emotions and attitudes online. How to model user preferenced information and provide personalized service is a crucial problem in big data era. In this paper, a new probabilistic model be proposed to model and analysis topic trends in personalized search. The model extended the Latent Dirichlet Allocation (LDA) model by introducing context variables, through which we can detect and analysis topic trends according to contextual information. The core idea of proposed probabilistic model is to learn a finite Dirichlet mixture model, and then adopt Bayesian discriminant to detect topic and topic trends analysis. Experimental results show that the proposed probabilistic mixture model can detect topics and discover topic trends effectively.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Dongling Chen AU - Wen Zeng PY - 2015/04 DA - 2015/04 TI - A Theme-Context Mixture Model for Personalized Search in Social Network BT - Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering PB - Atlantis Press SP - 202 EP - 206 SN - 2352-5401 UR - https://doi.org/10.2991/meic-15.2015.49 DO - 10.2991/meic-15.2015.49 ID - Chen2015/04 ER -