One size does not fit all: Multi-granularity search of web forums

Users rely increasingly on online forums, blogs, and mailing lists to exchange information, practical tips, and stories. Although this type of social interaction has become central to our daily lives and decision-making processes, forums are surprisingly technologically poor: often there is no choice but to browse through massive numbers of posts while looking for specific information. A critical challenge then for forum search is to provide results that are as complete as possible and that do not miss some relevant information but that are not too broad. In this paper, we address the problem of presenting textual search results in a concise manner to answer user needs. Specifically, we propose a new search approach over free-form text in forums that allows for the search results to be returned at varying granularity levels. We implement a novel hierarchical representation and scoring technique for objects at multiple granularities, taking into account the inherent containment relationship provided by the hierarchy. We also present a score optimization algorithm that efficiently chooses the best k-sized result set while ensuring no overlap between the results. We evaluate the effectiveness of multi-granularity search by conducting extensive user studies and show that a mixed granularity set of results is more relevant to users than standard post-only approaches.