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Improving social book search using structure semantics, bibliographic descriptions and social metadata

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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.

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Notes

  1. Searching an index against the set of search queries (topic set) and ranking using a weighing scheme is generally known as retrieval run or experiment. This paper uses run and experiment interchangeably.

  2. https://www.goodreads.com/api

  3. https://jsoup.org/

  4. https://www.oclc.org/en/dewey/resources/summaries.html#hun

  5. https://github.com/usnistgov/trec_eval

  6. https://github.com/terrierteam/jtreceval

  7. https://www.activestate.com

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Acknowledgments

This paper is based on the Ph.D. research work and thesis of the first author under the supervision of Prof. Dr. Shah Khusro. The first author acknowledges Shaheed Benazir Bhutto University, Sheringal for granting study leave with full pay, which enabled him to conduct research and perform retrieval experiments at Web Engineering Lab of the Department of Computer Science, University of Peshawar, Pakistan. The authors are thankful to Dr. Marijn Koolen from the Royal Netherlands Academy of Arts and Sciences and Dr. Jaap Kamps from the University of Amsterdam for giving access to the A/LT dataset. The authors appreciate their valuable discussions on the Social Book Search (SBS) Lab mailing list as well as via email in performing and evaluating SBS experiments. The authors are thankful for and acknowledge the valuable discussions with Dr. Craig Macdonald from the University of Glasgow on the Terrier JIRA forum about using the Terrier IR platform in SBS retrieval experiments.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Peshawar, Peshawar, 25120, Pakistan

    Irfan Ullah, Shah Khusro & Ibrar Ahmad

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  1. Irfan Ullah
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  2. Shah Khusro
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  3. Ibrar Ahmad
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Correspondence to Shah Khusro.

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Appendix

Appendix

Table 13 The top forty-eight baseline and re-ranking runs on the A/LT collection (Table 9)
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Table 14 The top forty-eight baseline and re-ranking runs on the updated A/LT collection (Table 10)
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Table 15 The comparison of the proposed and published best-performing baseline runs
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Table 16 Our proposed re-ranking runs against the re-ranking runs from the literature
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Ullah, I., Khusro, S. & Ahmad, I. Improving social book search using structure semantics, bibliographic descriptions and social metadata. Multimed Tools Appl 80, 5131–5172 (2021). https://doi.org/10.1007/s11042-020-09811-8

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