The emergence of social media allows users to get opinions, suggestions, or recommendations from other users about complex information needs. Tasks, as the CLEF Social Book Search 2016 Suggestion Track, propose to pursue this issue. The originality is to deal with verbose queries of book recommendation in order to support users in searching for books in catalogues of professional meta-data and complementary social media (i.e. tags, authors, similar products). In this context, a new technique for community-of-books discovery based on frequent social information (i.e. tags, authors) of similar books are proposed for book recommendation. Our method allows detecting frequent sub-graphs of similar books and using them to enrich the results returned by a traditional information retrieval system. This approach is tested on a collection containing Amazon/LibraryThing book descriptions and a set of queries, extracted from the LibraryThing discussion forums.