Ranking Documents Through Stochastic Sampling on Bayesian Network-based Models
Xing Tan, Jimmy Xiangji Huang, Aijun An · 2016
Using approximate inference techniques, we investigate in this paper the applicability of Bayesian Networks to the problem of ranking a large set of documents. Topology of the network is a bipartite. Network parameters (conditional probability distributions) are determined through an adoption of the weighting scheme tf-idf. Rank of a document with respect to a given query is defined as the corresponding posterior probability, which is estimated through performing Rejection Sampling. Experimental results suggest that performance of the model is at least comparable to the baseline ones such as BM25. The framework of this model potentially offers new and novel ways in weighting documents. Integrating the model with other ranking algorithms, meanwhile, is expected to bring in performance improvement in document ranking.