Using collaborative filtering in social book search

In this paper we describe our participation in INEX 2012 in the Social Book Search Track and the Linked Data Track. For the Social Book Search Track we compare the impact of query- and user-independent popularity measures and recommendations based on user profiles. Book suggestions are more than just topical relevance judgements and may include personal factors such as interestingness, fun and familiarity and book-related aspects such as quality and popularity. Our aim is to understand to what extent book suggestions are related to user-dependent and -independent aspects of relevance. Our findings are that evidence that is both query- and user-independent is not effective for improving a standard retrieval model using blind feedback. User-dependent evidence, on the contrary, is highly effective, leading to significant improvements. For the Linked Data Track we compare different methods of weighted result aggregation using the DBpedia ontology relations as facets and values. Facets and values are aggregated using either document counts or retrieval scores. The reason to use retrieval scores for facet ranking is that we want the top retrieved results to be summarised by the top ranked facets and values. In addition, we look at the impact of taking overlap in aggregation into account. Facet values that give access to many of the same documents have high overlap. Selecting facet values that have low overlap may avoid frustrating the user.