Expert Finding using discriminative infinite Hidden Markov Model
نویسندگان
چکیده
Process of finding the right expert for a given problem in an organization is becoming feasible. Using web surfing data it is feasible to find advisor who is most likely possessing the desired piece of fine grained knowledge related with given query. Web surfing data is clustered into tasks by using Gaussian Dirichlet process mixture model. In order to mine micro aspects in each task a novel discriminative infinite Hidden Markov Model is developed. The fine grained knowledge for each task can have hierarchical structure. In order to implement hierarchy apply the discriminative infinite Hidden Markov Model on micro aspects iteratively. Keywords—Advisor Search, Gaussian Dirichlet process mixture model, discriminative infinite Hidden Markov Model, Micro aspects , task, web surfing data, Clustering
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