Markov Model Document Retrieval

This paper presents a new probabilistic approachto document retrieval based on the assumption thata Markov process can explain the process by whichhumans rank the relevance of documents to queries.The model ranks documents for retrieval based on theirprobability of relevance. Two training 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.