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The Scientific World Journal
Volume 2015, Article ID 926418, 8 pages
http://dx.doi.org/10.1155/2015/926418
Research Article

Information Retrieval and Graph Analysis Approaches for Book Recommendation

1Aix-Marseille Université, CNRS, LSIS UMR 7296, 13397 Marseille, France
2Aix-Marseille Université, CNRS, CLEO OpenEdition UMS 3287, 13451 Marseille, France

Received 15 May 2015; Accepted 24 August 2015

Academic Editor: Mariofanna G. Milanova

Copyright © 2015 Chahinez Benkoussas and Patrice Bellot. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.