Cross-Document Search Engine For Book Recommendation
Chahinez Benkoussas, Patrice Bellot · Conference on Recommender Systems · 2015
A new combination of multiple Information Retrieval approaches are proposed for book recommendation based on complex users’ queries. We used dierent theoretical retrieval models: probabilistic as InL2 (Divergence From Randomness model) and language models and tested their interpolated combination. We considered the application of a graph based 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. Specically, this work tackles the problem of book recommendation in the context of CLEF Labs precisely Social Book Search track. We established a specic strategy for queries searching after separating query set into two genres \Analogue and \Non-Analogue after analyzing users’ needs. Series of reranking experiments demonstrate that combining retrieval models and exploiting linked documents for retrieving yield signicant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to dierent environments.