Building test collections based on nuggets is useful evaluating systems that return documents, answers, or summaries. However, nugget construction requires a lot of manual work and is not feasible for large query sets. Towards an efficient and scalable nugget-based evaluation, we study the applicability of semi-automatic nugget extraction in the context of the ongoing NTCIR One Click Access (1CLICK) task. We compare manually-extracted and semi-automatically-extracted Japanese nuggets to demonstrate the coverage and efficiency of the semi-automatic nugget extraction. Our findings suggest that the manual nugget extraction can be replaced with a direct adaptation of the English semi-automatic nugget extraction system, especially for queries for which the user desires broad answers from free-form text.

The One Click Access Task (1CLICK) of NTCIR requires systems to return a concise multi-document summary of web pages in response to a query which is assumed to have been submitted in a mobile context. Systems are evaluated based on information units (or iUnits), and are required to present important pieces of information first and to minimise the amount of text the user has to read. Using the official Japanese results of the second round of the 1CLICK task from NTCIR-10, we discuss our task setting and evaluation framework. Our analyses show that: (1) Simple baseline methods that leverage search engine snippets or Wikipedia are effective for 'lookup' type queries but not necessarily for other query types; (2) There is still a substantial gap between manual and automatic runs; and (3) Our evaluation metrics are relatively robust to the incompleteness of iUnits.

The NTCIR INTENT task comprises two subtasks: {\em Subtopic Mining}, where systems are required to return a ranked list of {\em subtopic strings} for each given query; and {\em Document Ranking}, where systems are required to return a diversified web search result for each given query. This paper summarises the novel features of the Second INTENT task at NTCIR-10 and its main findings, and poses some questions for future diversified search evaluation.

This paper tackles the problem of mining subgoals of a given search goal from data. For example, when a searcher wants to travel to London, she may need to accomplish several subtasks such as "book flights," "book a hotel," "find good restaurants" and "decide which sightseeing spots to visit." As another example, if a searcher wants to lose weight, there may exist several alternative solutions such as "do physical exercise," "take diet pills," and "control calorie intake." In this paper, we refer to such subtasks or solutions as subgoals, and propose to utilize sponsored search data for finding subgoals of a given query by means of query clustering. Advertisements (ads) reflect advertisers' tremendous efforts in trying to match a given query with implicit user needs. Moreover, ads are usually associated with a particular action or transaction. We therefore hypothesized that they are useful for subgoal mining. To our knowledge, our work is the first to use sponsored search data for this purpose. Our experimental results show that sponsored search data is a good resource for obtaining related queries and for identifying subgoals via query clustering. In particular, our method that combines ad impressions from sponsored search data and query co-occurrences from session data outperforms a state-of-the-art query clustering method that relies on document clicks rather than ad impressions in terms of purity, NMI, Rand Index, F1-measure and subgoal recall.

A variety of services utilizing users' positions have become available because of rapid advances in Global Positioning System (GPS) technologies. Since location information may reveal private information, preserving location privacy has become a significant issue. We proposed a dummy-based method of anonymizing location to protect this privacy in our previous work that generated dummies based on various restrictions in a real environment. However, the previous work assumed a simplified mobility model in which users kept moving and did not stop. If we assume a more realistic mobility model in which users often pause to visit various attractions, it becomes increasingly more difficult to generate dummies that will move naturally. In this paper, we assumed that the users' movements are known in advance and propose a dummy-based anonymization method based on user movements, where dummies move naturally while stopping at several locations. We simulated user movements on real map information and verified the method we propose was more effective than the previous one.

A diversified search result for an underspecified query generally contains web pages in which there are answers that are relevant to different aspects of the query. In order to help the user locate such relevant answers, we propose a simple extension to the standard Search Engine Result Page (SERP) interface, called AspecTiles. In addition to presenting a ranked list of URLs with their titles and snippets, AspecTiles visualizes the relevance degree of a document to each aspect by means of colored squares ("tiles"). To compare AspecTiles with the standard SERP interface in terms of usefulness, we conducted a user study involving 30 search tasks designed based on the TREC web diversity task topics as well as 32 participants. Our results show that AspecTiles has some advantages in terms of search performance, user behavior, and user satisfaction. First, AspecTiles enables the user to gather relevant information significantly more efficiently than the standard SERP interface for tasks where the user considers several different aspects of the query to be important at the same time (multi-aspect tasks). Second, AspecTiles affects the user's information seeking behavior: with this interface, we observed significantly fewer query reformulations, shorter queries and deeper examinations of ranked lists in multi-aspect tasks. Third, participants of our user study found AspecTiles significantly more useful for finding relevant information and easy to use than the standard SERP interface. These results suggest that simple interfaces like AspecTiles can enhance the search performance and search experience of the user when their queries are underspecified.

People carry around mobile devices all the time in their daily life and get various information from the Internet in various situations. When searching for information (content) by using mobile devices, users' activities (e.g., walking and standing) and their situations (e.g., commuting in the morning and going out downtown in the evening) often change and this change may affect their degree of concentration on the display of mobile devices and their information needs. Therefore, search systems should provide users with an amount of information suitable for their activities and with a type of information suitable for their situations. In this paper, we present the design and implementation of a content search system considering mobile users' activities and situations, which aims to reduce users' load of operations in content searching. Our system recognizes user's activities and switches between two kinds of content search systems according to the user's activity: the location-based content search system runs when the user is standing, while the menu-based content search system runs when the user is walking. Both systems present information based on the user's situation. We also introduce a user's activity recognition method for mobile devices. This method classifies user's activity into standing, walking, and running using the sensors equipped in mobile devices.

Smart-phone users can search for information about surrounding facilities or a route to their destination. However, it is difficult to get or search for information while walking because of low legibility. To address this problem, users have to stop walking or enlarge the screen. Our previously proposed system for smart-phone switches the information presentation policies in response to the user's context. In this paper we describe our context recognition mechanism for this system. This mechanism estimates user context from sensors embedded in a smart-phone. We use a Support Vector Machine for the context classification and compare four types of feature values consisting of FFT and 3 types of Wavelet Transforms. Experimental results show that recognition rates are 87.2 % with FFT, 90.9 % with Gabor Wavelet, 91.8 % with Haar Wavelet, and 92.1 % with MexicanHat Wavelet.

Map-based application is one of the most popular applications using a built-in GPS receiver. Users often use applications based on geographic information in situations where they share the same information needs with friends or families and conduct some tasks together. However, it is difficult to communicate geographic information with each other because existing map-based applications are basically designed for solitary work. In this paper, to support collaborative work using map-based applications, we explore important factors for collaborative use of map interface. We conducted a user experiment using a map-based application implemented with some functions for supporting collaborative work. The result shows that it is important to i) enable users to share information among multiple devices and view it individually on their own displays when they use a map together, and ii) synchronize users' displays and map operations when they communicate geographic information with each other.

Recent mobile devices are mostly equipped with a GPS receiver. This trend has arisen a variety of location based services (LBSs) which enable users to search local information of their current locations. To use LBSs, a user needs to send his/her location information to a service provider. Users' location information is inherently private, since it can reveal the critical information. In this paper, we propose a method to protect the user's location privacy by sending the user's location with dummy locations, which are determined based on the user's current location. We conduct an experiment to evaluate the effectiveness of our proposed method using real users' trajectories on a real map.