A new combination of multiple Information Retrieval approaches are proposed for book recommendation based
on complex users’ queries. We used different 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. Specifically, this
work tackles the problem of book recommendation in the context
of CLEF Labs precisely Social Book Search track. We established
a specific strategy for queries searching after separating query
set into two genres “Analogue” and “Analogue” after analyzing
users’ needs. Series of reranking experiments demonstrate that
combining retrieval models and exploiting linked documents for
retrieving yield significant improvements in terms of standard
ranked retrieval metrics. These results extend the applicability
of link analysis algorithms to different environments