Users of a digital book library system typically interact with the system to search for books by querying on the metadata describing the books or to search for information in the pages of a book by querying using one or more keywords. In either cases, a large volume of results are returned of which, the results relevant to the user are not often among the top few. Re-ranking of the search results according to the user’s interest based on his relevance feedback, has received wide attention in information retrieval. Also, recent work in collaborative filtering and information retrieval has shown that sharing of search experiences among users having similar interests, typically called a community, reduces the effort put in by any given user in retrieving the exact information of interest. In this paper, we propose a collaborative filtering based reranking strategy for the search processes in a digital library system. Our approach is to learn a user profile representing user’s inte...
U. Rohini, Vamshi Ambati