Search

Online book search services allow users to tag and review books but do not include such data in the search index, which only contains titles, author names and professional subject descriptors. Such professional metadata is a limited description of the book, whereas tags and reviews can describe the content in more detail and cover many other aspects such as quality, writing style and engagement. In this paper we investigate the impact of including such user-generated content in the search index of a large collection of book records from Amazon and LibraryThing. We find that professional metadata is often too limited to provide good recall and precision and that both user reviews and tags can substantially improve performance. We perform a detailed analysis of different types of metadata and their impact on a number of topic categories and find that user-generated content is effective for a range of information needs. These findings are of direct relevance to large online book sellers and social cataloguing sites. [ABSTRACT FROM AUTHOR]

With massive book digitization efforts underway, the need for effective retrieval of books and pages in books is an important problem. This paper describes our submissions to the INEX 2007 Book Search track. We explored using book specific features such as table of content and index pages and headers along with non-book specific features. Our results show that indexing the entire contents of books and headers provided the most effective retrieval strategy. [ABSTRACT FROM AUTHOR]

Through mass-digitization projects and with the use of OCR technologies, digitized books are becoming available on the Web and in digital libraries. The unprecedented scale of these efforts, the unique characteristics of the digitized material as well as the unexplored possibilities of user interactions make full-text book search an exciting area of information retrieval (IR) research. Emerging research questions include: How appropriate and effective are traditional IR models when applied to books? What book specific features (e.g., back-of-book index) should receive special attention during the indexing and retrieval processes? How can we tackle scalability? In order to answer such questions, we developed an experimental platform to facilitate rapid prototyping of a book search system as well as to support large-scale tests. Using this system, we performed experiments on a collection of 10 000 books, evaluating the efficiency of a novel multi-field inverted index and the effectiveness of the BM25F retrieval model adapted to books, using book-specific fields. [ABSTRACT FROM AUTHOR]