Exploiting multi-evidence from multiple user’s interests to personalizing information retrieval

Abstract

The goal of personalization in information retrieval is to tailor the search engine results to the specific goals, preferences and general interests of the users. We propose a novel model that considers the user's interests as sources of evidence in order to tune the accuracy of documents returned in response to the user query. The model's fundation comes from influence diagrams which are extension of Bayesian graphs, dedicated to decision-making problems. Hence, query evaluation is carried out as an inference process that aims to computing an aggregated utility of a document by considering its relevance to the query but also the corresponding utility with regard to the user's topics of interest. Experimental results using enhanced TREC collections indicate that our personalized retrieval model is effective.