Improving social book search using structure semantics, bibliographic descriptions and social metadata
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Improving social book search using structure semantics, bibliographic descriptions and social metadata Irfan Ullah 1
& Shah Khusro
1
& Ibrar Ahmad
1
Received: 9 May 2020 / Revised: 18 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Social Book Search is an Information Retrieval (IR) approach that studies the impact of the Social Web on book retrieval. To understand this impact, it is necessary to develop a stronger classical baseline run by considering the contribution of query formulation, document representation, and retrieval model. Such a stronger baseline run can be re-ranked using metadata features from the Social Web to see if it improves the relevance of book search results over the classical IR approaches. However, existing studies neither considered collectively the contribution of the three mentioned factors in the baseline retrieval nor devised a re-ranking formula to exploit the collective impact of the metadata features in re-ranking. To fill these gaps in the literature, this research work first performs baseline retrieval by considering all three factors. For query formulation, it uses topic sets obtained from the discussion threads of LibraryThing. For book representation in indexing, it uses metadata from social websites including Amazon and LibraryThing. For the role of the retrieval model, it experiments with traditional, probabilistic, and fielded models. Second, it devises a re-ranking solution that exploits ratings, tags, reviews, and votes in reordering the baseline search results. Our best-performing retrieval methods outperform existing approaches on several topic sets and relevance judgments. The findings suggest that using all topic fields formulates the best search queries. The user-generated content gives better book representation if made part of the search index. Re-ranking the classical/baseline results improves relevance. The findings have implications for information science, IR, and Interactive IR. Keywords Information retrieval . Information science . Social web . Social book search . Ranking, re-ranking
1 Introduction The Social Web has brought new prospects for and motivated Information Retrieval (IR), Information Science (IS), and Interactive IR researchers. One of the main factors behind this
* Shah Khusro [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
motivation is the ambiguity of the natural language that makes it challenging for existing retrieval solutions to accurately interpret the search queries and get relevant books. In addition, Library & IS (LIS) experts professionally curate the bibliographic descriptions of the books, also known as professional metadata. These descriptions result in vocabulary mismatch with the natural language queries of the users when they search for books [32]. Therefore, the book readers moved to social book websites to discuss books, share their reading experiences, and information ne
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