부산대학교 도서관

Faceted search has been used successfully for many vertical applications such as e-commerce and digital libraries. However, it remains challenging to extend faceted search to the open-domain web due to the large and heterogeneous nature of the web. Recent work proposed an alternative solution that extracts facets for queries from their web search results, but neglected the precision-oriented perspective of the task -- users are likely to care more about precision of presented facets than recall. We improve query facet extraction performance under a precision-oriented scenario from two perspectives. First, we propose an empirical utility maximization approach to learn a probabilistic model by maximizing the expected performance measure instead of likelihood as used in previous approaches. We show that the empirical utility maximization approach can significantly improve over the previous approach under the precision-oriented scenario. Second, instead of showing facets for all queries, we propose a selective method that predicts the extraction performance for each query and selectively shows facets for some of them. We show the selective method can significantly improve the average performance with fair coverage over the whole query set.