A Theme-Context Mixture Model for Personalized Search in Social Network

DOI

10.2991/meic-15.2015.49How to use a DOI?

Keywords

Mixture Mode; Contextual Mining; LDA; PLSA; User Preference Information

Abstract

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.

Copyright

© 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/).

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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  -