Expert Finding System with Multi-queries Extension and Hybrid Candidate Selection

The thesis proposes two models to solve the expert finding problem. One is query extension model; the other is hybrid candidate selection model. These models base on documents. They find experts with extension query terms and some profiles. Moreover, every model designs two methods to compare the influence of extension query terms in the system. One method has the same weighted extension query terms; the other has different weighted extension query terms. Chapter 3 presented that Query extension model has three phases to find experts. In the first phase, the system needs to construct the dataset of extension query terms. The processes have to split sentences, analyze part of speech, and retrieve relevant extension query terms with C-value. In the second phase, the system calculates the relationship between extension query terms and relevant documents with the dot product of two vectors. Afterwards the system ranks relevant documents with the calculation result. In the third phase, the system combines the rank from the two phase and profiles from original dataset to calculate every candidate’s score. It ranks these scores for candidates and imports a recommendation list. Hybrid candidate selection model has four phases. It is the same as query extension model in the previous three phases. In the fourth phase, the system collects document scores and profile scores gotten from relevant documents for every candidate. Then, it ranks these scores and selects recommended experts. In the chapter 4 and 5, by the results of P@n, R@n, MRR, and MAP, we found that the precision of hybrid candidate selection model is over 3% higher than query extension model. Different weighted extension query terms have positive influences for hybrid candidate selection model. However they do not have greater impacts for query extension model. Weighted hybrid candidate selection method can get higher precision in all proposed methods as a whole. It can also keep similar precisions when the number of candidates is increasing. In the last chapter, it summarizes seven points and suggests five ideas about future works.