Probabilistic Reuse of Past Search Results

Abstract

In this paper, a new Monte Carlo algorithm to improve precision of information retrieval by using past search results is presented. Experiments were carried out to compare the proposed algorithm with traditional retrieval on a simulated dataset. In this dataset, documents, queries, and judgments of users were simulated. Exponential and Zipf distributions were used to build document collections. Uniform distribution was applied to build the queries. Zeta distribution was utilized to simulate the Bradford’s law representing the judgments of users. Empirical results show a better performance of our algorithm compared with traditional retrieval.