Exploiting social media and tagging for social book search: Simple query methods for retrieval optimization

With web and social media information availability and accessibility increasing on the one hand, there is an increased complexity of retrieval on the other. Generally, real information needs are complex to express. Books are the prevailing information resources and book searching is one of the online activities that users attempt daily. Mobile technology makes it easy to handle books, i.e., ibook and kindle formats; however, locating users' preference over the Internet is still quite complex. Efforts are being made to help users locate their desired books easily and quickly. This research is set up to investigate techniques to support users in searching and navigating the full texts of digitized books and complementary social media in order to enhance the user book search experience. The idea is based on the INEX SBS track to use professional metadata and user-generated metadata to enhance the retrieval process of books by optimizing simple search query with INEX SBS 2015. Amazon and LibraryThing book descriptions were processed to extract information and important fields to be indexed. The proposed model use the Named Entity Recognition tagger (NER) and the Part-Of-Speech tagger (POS) to extract relevant topics that are related to book search. The results indicated that using simple methods such as NER and POS tagging can generate an effective query for book retrieval.