Markov model document retrieval

This paper presents a new probabilistic approach to document retrieval based on the assumption that, a Markov process can explain the process by which humans rank the relevance of do cuments to queries. The model ranks documents for retrieval based on their probability of r elevane. Two truining methods are presented. The model is compared with Latent Semantic Analysis (LSA) on two publicly available databases. The results show that, the new algorithm achieves Precision/Recall performance equivalent to or better than LSA.