Xuchu Dong

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Session search is an information retrieval task that involves a sequence of queries for a complex information need. It is characterized by rich user-system interactions and temporal dependency between queries and between consecutive user behaviors. Recent efforts have been made in modeling session search using the Partially Observable Markov Decision(More)
— In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the problem of optimal triangulation of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving(More)
According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolution­ ary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these(More)
This year we participate in the TREC Session Track Task 1. We adopt the Query Change Model (QCM), weighted QCM, re-ranking, clustering, and error analysis in our approaches. The QCM retrieval model is employed to combine all queries in a session. QCM allows documents that are relevant to any query in a session to appear in the final retrieval list. Weighted(More)
To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSum Weight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics(More)
For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population diversity to identify stagnation and convergence as well as to guide the search procedure. Experiments on representative benchmarks show that DHGA(More)
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