TEM主题专业模型

TopicExpertiseModel =================== /** Copyright (C) 2013 by SMU Text Mining Group/Singapore Management University/Peking University TopicExpertiseModel is distributed for research purpose, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. If you use this code, please cite the following paper: Liu Yang, Minghui Qiu, Swapna Gottipati, Feida Zhu, Jing Jiang, Huiping Sun and Zhong Chen. CQARank: Jointly Model Topics and Expertise in Community Question Answering. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013). (http://dl.acm.org/citation.cfm?id=2505720) Feel free to contact the following people if you find any problems in the package. yang.liu@pku.edu.cn * */ Brief Introduction =================== 1. Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. 2. This package implements Gibbs sampling for Topic Expertise Model for jointly modeling topics and expertise in question answering communities. More details of our model are described in the following paper: Liu Yang, Minghui Qiu, Swapna Gottipati, Feida Zhu, Jing Jiang, Huiping Sun and Zhong Chen. CQARank: Jointly Model Topics and Expertise in Community Question Answering. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013). (http://dl.acm.org/citation.cfm?id=2505720)