We consider the problem of retrieving multiple documents relevant to the single subtopics of a given web query, termed “full-subtopic retrieval”. To solve this problem we present a novel search results clustering algorithm that generates clusters labeled by keyphrases. The keyphrases are extracted from the generalized suffix tree built from the search results and merged through an improved hierarchical agglomerative clustering procedure. We also introduce a novel measure for evaluating full-subtopic retrieval performance, namely “Subtopic Search Length under k document sufficiency”. Using a test collection specifically designed for evaluating subtopic retrieval, we found that our algorithm outperformed both other existing search results clustering algorithms and also a search results re-ranking method that emphasized diversity of results (at least for k>1; i.e., when we are interested in retrieving more than one relevant document per subtopic). Our approach has been implemented into KeySRC (Keyphrase-based Search Results Clustering), a full web clustering engine available online at

Sensor networks have been conventionally defined as a network of sensor motes that collaboratively detect events and report them to a remote monitoring station. This paper makes an attempt to extend this notion to the social context by using mobile phones as a replacement for motes. We envision a social application where mobile phones collaboratively sense their ambience and recognize socially “interesting ” events. The phone with a good view of the event triggers a video recording, and later, the video-clips from different phones are “stitched ” into a video highlights of the occasion. We observe that such a video highlights is akin to the notion of event coverage in conventional sensor networks, only the notion of “event ” has changed from physical to social. We have built a Mobile Phone based Video Highlights system (MoVi) using Nokia phones and iPod Nanos, and have experimented in real-life social gatherings. Results show that MoVi-generated video highlights (created offline) are quite similar to those created manually, (i.e., by painstakingly editing the entire video of the occasion). In that sense, MoVi can be viewed as a collaborative information distillation tool capable of filtering events of social relevance.

The SIGSPATIAL Special is the newsletter of the Association for Computing Machinery

We discuss which are the main research themes in the field of search results clustering and report some recent results achieved by the Information Mining group at Fondazione

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.

Taxonomies can serve as browsing tools for document collections. However, given an arbitrary collection, pre-constructed taxonomies could not easily adapt to the specific topic/task present in the collection. This paper explores techniques to quickly derive task-specific taxonomies supporting browsing in arbitrary document collections. The supervised approach directly learns semantic distances from users to propose meaningful task-specific taxonomies. The approach aims to produce globally optimized taxonomy structures by incorporating path consistency control and usergenerated task specification into the general learning framework. A comparison to stateof-the-art systems and a user study jointly demonstrate that our techniques are highly effective. 1