Expert Finding using discriminative infinite Hidden Markov Model

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 I. INTRODUCTION In a collaborative environment every person shares information with every other person. It is common that different members of the group may try to access the same information separately. For example, this happens in a research lab, where members are focused on projects which require similar background knowledge. It may happen that the researcher tries to solve a particular problem using one method which he/ she is not familiar with but has been studied by another researcher. In this case finding the right individual who already knows something in that field is always superior than studying by oneself. Because people can provide live interaction, better communication and give more insights on that particular problem and also gives what were the problems faced by him/her. Finding the right person is always hard due to a number of reasons. This scenario is different from conventional expert (14) search problem in that finding expert (16) on particular problem is dependent on associated documents in the enterprise repository. Our goal is to search advisors who know something related to that query. In order to analyze knowledge acquired by web users we will analyze users web surfing (1) and browsing activities which will reveal users knowledge gaining process on micro aspect level. For finding web surfing activities of each user one can use tcpdump for Linux platform and windump for Windows platform.Latent semantic structures in the Web surfing shows people's knowledge gaining process and web surfing data is good improvement over documents. A two step framework for mining micro aspect in each task has been proposed. In the first step, Infinite Gaussian mixture model (3) based on Dirichlet Process (4) has been designed to cluster sessions which are generated by Web surfing activities. In the second step, micro aspect from session in each task has been extracted. A novel discriminative infinite Hidden Markov Model (d-iHMM ) is applied to the micro aspects for mining and patterns in each task. Finally a language model (19) based expert search method is applied over mined aspects to search for advisor. The aim of topic is not to find persons who are experts but to find persons who have desired knowledge related to the query. Also implementing fine-grained knowledge in hierarchical way is difficult because knowledge can contain micro aspects with similar topics. This problem can be solved by applying d-iHMM model iteratively. In order to solve d-iHMM Beam sampling (5) is used.