It is expected that more and more people will search the web when they are on the move. Though conventional search engines can be directly visited from mobile devices with web browsing capabilities, the information is not as conveniently accessible from a handheld device as it is from desktops. Existing information discovery mechanisms for searching the web are not well-suited to mobile devices. In this paper, a block importance model is employed to assign importance values to different segments of a web page, in order to extract and present more condensed search results to mobile users. Based on the block importance model, three presentations for displaying the result pages in different levels of detail have been designed to reduce both the number of user interactions and the overall search time. A set of user study experiments have been carried out to compare the three presentations and a commercial service on typical mobile devices. Experimental results show that our approaches can help users to explore web search results more efficiently. 1.

Due to the appearance and widespread diffusion of new mobile devices (PDAs, smartphones etc.), the traditional notion of computing is quickly fading away, giving birth to new paradigms and to new non-trivial problems.

Abstract. One of the key factors for effective personalization of information access is the user context. We propose a framework which integrates several critical elements that make up the user context, namely, the user’s short-term behavior, semantic knowledge from ontologies that provide explicit representations of the domain of interest, and long-term user profiles revealing interests and trends. Our proposed approach involves implicitly building ontological user profiles by assigning interest scores to existing concepts in a domain ontology. These profiles are, therefore, maintained and updated as annotated instances of a reference domain ontology. We propose a spreading activation algorithm for maintaining the interest scores in the user profile based on the user’s ongoing behavior. Our experimental results show that the user context can be effectively utilized for Web search personalization. Specifically, re-ranking the search results based on interest scores derived from the semantic evidence in an ontological user profile provides better search results by proficiently bringing results closer to the top when they are most relevant to the user. 1

Search systems need to have the flexibility to adapt to each query so a search strategy that is likely to provide the most useful retrieval results can be employed. By Lyndon Kennedy, Student Member IEEE, Shih-Fu Chang, Fellow IEEE, and Apostol Natsev ABSTRACT | We conduct a broad survey of query-adaptive search strategies in a variety of application domains, where the internal retrieval mechanisms used for search are adapted in response to the anticipated needs for each individual query experienced by the system. While these query-adaptive approaches can range from meta-search over text collections to multimodal search over video databases, we propose that all such systems can be framed and discussed in the context of a single, unified framework. In our paper, we keep an eye towards the domain of video search, where search cues are available from a rich set of modalities, including textual speech transcripts, low-level visual features, and high-level semantic concept detectors. The relative efficacy of each of the modalities is highly variant between many types of queries. We observe that the state of the art in query-adaptive retrieval frameworks for video collections is highly dependent upon the definition of classes of queries, which are groups of queries that share similar optimal search strategies, while many applications in text and web retrieval have included many advanced strategies, such as direct prediction of search method performance and inclusion of contextual cues from the searcher. We conclude that such advanced strategies previously developed for text retrieval have a broad range of possible applications in future research in multimodal video search.

In this thesis, we show how context data, taken into account its specific characteristics, can be used to provide proactive answers and rank existing query answers based on their relevance to the users, via a user representation.

Towards an architecture for personalized information retrieval implying the user’s profile and votes