Exploiting information induced from (query-specific) clustering of top-retrieved documents has long been proposed as means for improving precision at the very top ranks of the returned results. We present a novel language model approach to ranking query-specific clusters by the presumed percentage of relevant documents that they contain. While most previous cluster ranking approaches focus on the cluster as a whole, our model also exploits information induced from documents associated with the cluster. Our model substantially outperforms previous approaches for identifying clusters containing a high relevant-document percentage. Furthermore, using the model to produce document ranking yields precision-at-top-ranks performance that is consistently better than that of the initial ranking upon which clustering is performed; the performance also favorably compares with that of a state-of-the-art pseudo-feedback retrieval method.

Web searches from mobile devices such as PDAs and cell phones are becoming increasingly popular. However, the traditional list-based search interface paradigm does not scale well to mobile devices due to their inherent limitations. In this article, we investigate the application of search results clustering, used with some success for desktop computer searches, to the mobile scenario. Building on CREDO (Conceptual Reorganization of Documents), a Web clustering engine based on concept lattices, we present its mobile versions Credino and SmartCREDO, for PDAs and cell phones, respectively. Next, we evaluate the retrieval performance of the three prototype systems. We measure the effectiveness of their clustered results compared to a ranked list of results on a subtopic retrieval task, by means of the device-independent notion of subtopic reach time together with a reusable test collection built from Wikipedia ambiguous entries. Then, we make a crosscomparison of methods (i.e., clustering and ranked list) and devices (i.e., desktop, PDA, and cell phone), using an interactive information-finding task performed by external participants. The main finding is that clustering engines are a viable complementary approach to plain search engines both for desktop and mobile searches especially, but not only, for multitopic informational queries.

Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.