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The Importance of Document Ranking and User-Generated Content for Faceted Search and Book Suggestions

机译：文档排名和用户生成的内容的重要性，以便进行刻面搜索和书籍建议

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In this paper we describe our participation in INEX 2011 in the Books and Social Search Track and the Data Centric Track. For the Books and Social Search Track we focus on the impact of different document representations of book metadata for book search, using either professional metadata, user-generated content or both. We evaluate the retrieval results against ground truths derived from the recommendations in the LibraryThing discussion groups and from relevance judgements obtained from Amazon Mechanical Turk. Our findings show that standard retrieval models perform better on user-generated metadata than on professional metadata. For the Data Centric Track we focus on the selection of a restricted set of facets and facet values that would optimally guide the user toward relevant information in the Internet Movie Database (IMDb). We explore different methods for effective result summarisation by means of weighted aggregation. These weighted aggregations are used to achieve maximal coverage of search results, while at the same time penalising overlap between sets of documents that are summarised by different facet values. We found that weighted result aggregation combined with redundancy avoidance results in a compact summary of available relevant information.
机译：在本文中，我们描述了我们在书籍和社会搜索轨道上的2011年Inex 2011和以数据为中心的轨道。对于书籍和社交搜索轨道，我们专注于使用专业元数据，用户生成的内容或两者的书籍搜索的不同文档表示的影响。我们评估从图书馆讨论小组中的建议中获得的地面真理以及从亚马逊机械土耳其人获得的相关判断的地面真理评估了检索结果。我们的调查结果表明，标准检索模型在用户生成的元数据上更好地表现优于专业元数据。对于数据为中心的曲目，我们专注于选择受限制的小平面和小平面值，这些方面和面部值最佳地指导用户朝着因特网电影数据库（IMDB）中的相关信息。我们通过加权聚合探索有效结果汇总的不同方法。这些加权聚合用于实现搜索结果的最大覆盖范围，同时惩罚由不同方面值总结的文档组之间的重叠。我们发现加权结果聚合与冗余避免相结合的可用相关信息的紧凑概要。

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