Singampalli, Lakshmi Lavanya2017-11-272017-11-272014-08https://hdl.handle.net/11299/191214University of Minnesota M.S. thesis.August 2014. Major: Computer Science. Advisor: Carolyn Crouch. 1 computer file (PDF); vii, 43 pages.Information Retrieval as an area of research aims at satisfying the information need of a user. Retrieval in the Information Age has expanded exponentially as its underlying technologies have expanded. Traditional IR systems that give response to a user's natural language search query are combined with recommendation through collaborative filtering [6]. This research focuses on a methodology that combines both traditional IR and recommender systems. It is done as part of the Social Book Search (SBS) Track, Suggestion task of INEX (INitiative for the Evaluation of XML Retrieval) 2014 [3]. The Social Book Search Track was introduced by INEX in 2011 with the purpose of providing support to users in terms of easy search and access to books by using metadata. One complexity of the task lies in handling both professional and social metadata which are different in terms of both kind and quantity. Methodology and experiments discussed are inspired by background research [1,2,4,5,6] on the Social Book Search track. Our IR team submitted six runs for the track to the INEX 2014 competition, five of which use a recommender system that re-ranks the otherwise traditional set of results. Background work done to establish a good foundation for the methodology used in the SBS 2014 task includes experiments performed on both the 2011 and 2013 Social Book Search tracks. This research focuses on the 2013 experiments and their impact on results produced for SBS 2014.enThesis or Dissertation