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.

社交图书搜索是一种信息检索（IR）方法，用于研究社交网络对图书检索的影响。为了理解这种影响，有必要通过考虑查询表述，文档表示和检索模型的贡献来开发更强大的经典基线。可以使用来自社交网站的元数据功能对这种更强的基线运行进行重新排名，以查看它是否比传统的IR方法提高了图书搜索结果的相关性。但是，现有研究既未集体考虑上述三个因素在基线检索中的贡献，也未设计出重新排名公式来利用元数据功能在重新排名中的集体影响。为了填补文献中的空白，这项研究工作首先通过考虑所有三个因素来进行基线检索。对于查询表述，它使用从LibraryThing的讨论线程获得的主题集。对于索引中的书籍表示，它使用来自社交网站（包括Amazon和LibraryThing）的元数据。对于检索模型的作用，它会尝试使用传统的，概率性的和现场的模型。其次，它设计了一种重新排名解决方案，该方案利用评分，标签，评论和投票对基线搜索结果进行重新排序。我们在多个主题集和相关性判断方面的最佳检索方法优于现有方法。调查结果表明，使用所有主题字段可以制定最佳的搜索查询。如果将用户生成的内容作为搜索索引的一部分，则可以提供更好的书籍表示。对经典/基线结果重新排序可以提高相关性。这些发现对信息科学，投资者关系，