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Topic Expertise Model
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.
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 related publication http://dl.acm.org/citation.cfm?id=2505720.
Related Publication: Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., & Chen, Z. (2013). CQArank: jointly model topics and expertise in community question answering. Paper presented at the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, California, USA.
Available in InK: http://ink.library.smu.edu.sg/sis_research_smu/30/