Information retrieval is the science of retrieving documents or information from a corpus based on the need of user. Selecting a book from a collection of available books based on its topical relevance to the query may not give us the "best" (or all the "best") such book(s). However, by including social data, such as popularity, reviws and ratings, may improve the results. So we include social data with book metadata for this purpose. The main goal of this research is to provide a book retrieval system for the Social Book Search (SBS) Track of the INEX forum. For the SBS track, participants are provided with an XML collection of data from Amazon and LibraryThing (LT) forum, a set of topics from the LT forum enriched with user catalogue data (i.e., books that the topic creator has in his LibraryThing personal catalogue), and anonymous user profiles. Participants must devise a system which provides the ISBN/work IDs of the books which are relevant to the topic creator. For this purpose, we designed a recommender system which provides personalized search results.
University of Minnesota M.S. thesis. August 2014. Major: Computer Science. Advisor: Carolyn Crouch. 1 computer file (PDF); vi, 41 pages.
Personalized Book Retrieval System Using Amazon-LibraryThing Collection.
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