Web 2.0 applications are a rich source of multimedia resources, that describe sights, events, whether conditions, traffic situations and other relevant objects along the user's route. Compared to static sight descriptions, Web 2.0 resources can provide up-to-date visual information, which has been found important or interesting by the other users. Some algorithms have been suggested recently for the landmark finding problem from photos. Still, if users want related videos or background information about a particular place of interest it is necessary to contact different social platforms or general search engines. In this paper we present GuideMe! - a mobile application that automatically identifies landmark tags from Flickr groups and gathers relevant sightseeing resources from various Web 2.0 social platforms.

With the growing amount of information on users' desktops and increasing scale and complexity of intranets, Enterprise and Desktop Search are becoming two increasingly important Information Retrieval applications. While the challenges arising there are not completely different from those that the web community has faced for years, advanced web search solutions are often unable to address them properly. In this tutorial we give a research prospective on distinctive features of both Enterprise and Desktop Search, explain typical search scenarios, and review existing ranking techniques and algorithms.

This work investigates personalized social search based on the user's social relations -- search results are re-ranked according to their relations with individuals in the user's social network. We study the effectiveness of several social network types for personalization: (1) Familiarity-based network of people related to the user through explicit familiarity connection; (2) Similarity-based network of people "similar" to the user as reflected by their social activity; (3) Overall network that provides both relationship types. For comparison we also experiment with Topic-based personalization that is based on the user's related terms, aggregated from several social applications. We evaluate the contribution of the different personalization strategies by an off-line study and by a user survey within our organization. In the off-line study we apply bookmark-based evaluation, suggested recently, that exploits data gathered from a social bookmarking system to evaluate personalized retrieval. In the on-line study we analyze the feedback of 240 employees exposed to the alternative personalization approaches. Our main results show that both in the off-line study and in the user survey social network based personalization significantly outperforms non-personalized social search. Additionally, as reflected by the user survey, all three SN-based strategies significantly outperform the Topic-based strategy.

The desktop search tools provide powerful query capabilities and result presentation techniques. However, they do not take the user context into account. We propose to exploit collected information about user activities with desktop files and applications for activity-based desktop search. When I prepare for a project review and type in a search box the name of a colleague, I expect to find her last deliverable draft, but not her email with a paper review or our joint conference presentation. Ideally, the desktop search system should be able to infer my current task from the logs of my previous activities and present task-specific search results.