New algorithms for composite retrieval

Internet users constantly make searches to find objects or results of their interest, generally through terms or phrases. Traditional search offers only solutions that take into account just the individual characteristics of the results, and not the relations they have with the rest of the universe. Typically, we are given an ordered list of the results related to the search criterion, which implies the need of changing several times the terms of the query to get to a solution that is more adequate to the intended search goal. As a solution to this problem, Composite Retrieval proposes that the results to a query may be grouped in sets of items (bundles), related through some similarity criterion, but at the same time are complementary. In this work we propose heuristic algorithms for Composite Retrieval which are evaluated experimentally, showing performance improvements over the previous results presented in the literature.