In recent years, running has become increasingly popular as an effective exercise activity which could help improve and maintain one’s physical health. However, even if we understand that exercising regularly has many benefits to our body and brain, it is not easy for people to stay motivated. To deal with this issue, in our previous research, we developed a running support system based on the users’ own records. Our system enables users to run and compete against virtual runners in an audio-based augmented reality space. However, in the AR running support system, competing with real users is more immersive than competing against one’s own running records. In this study, we aim to improve the motivation of running users, not only using the users’ own records, we also use the GPS information and time data from other real runners. We believe that by using information from real runners, our virtual running rival generation/recommendation method can improve the enthusiasm of users. Therefore, we propose a rival recommendation approach for our acoustic-based AR running support system considering the athletic ability of users. In addition, as a preliminary analysis, we conduct a questionnaire survey to clarify users’ needs for the future development of our running support system.

Recent years have witnessed a rapid increase in the use of car navigation systems, which provide drivers with directions to their destinations. However, such systems do not always recommend a route that perfectly matches a driver's intent. Even when drivers intentionally change the driving route from the recommended one to another, most car navigation systems keep recommending or lead them back to the original recommended route. Such recommendations may not adequately reflect a driver's intent. We previously proposed a route recommendation method based on the estimation of a driver's intent by comparing the characteristics of a route selected by a driver and a route not selected by the driver but recommended by the car navigation system. In this study, we propose a method that can consider multiple costs and learn a driver's concept of the values for each cost; in brief, it represents an effective method for learning drivers' route selection preferences. In addition, we describe experimental evaluation results of our proposed method for driving route recommendations and learning drivers' route selection preferences.

This paper presents TWinChat, a Twitter and Web user interactive chat system to support simultaneous communication between microbloggers and Web users in real-time through both the contents of microblogs and Web pages. TWinChat provides a question answering interface attached to Web pages, which allows Web users to chat with Twitter users in real-time while presenting tweets that are associated with Web pages, i.e., simultaneous cross-media communication. In order to map heterogeneous media, the system extracts relationship between tweets and Web pages by generating queries based on location names. Thus, our system can effectively present messages from Web users to help Twitter users immediately obtain useful information or knowledge, and it also can effectively present tweets from the Twitter users to help the Web users easily grasp the current situation in real-time.

We have developed a method that integrates Web search with car navigation systems. The method allows for automatic retrieval and display of relevant Web pages without the need to input keywords. We assume that the operations a user performs on a car navigation system's digital map reflect specific intentions which can be determined through analysis and interpretation of the user operation sequence, in turn providing a basis for the automatic generation of Web queries and display of relevant Web pages. In this demo, we explain the method's implementation on a real car navigation system and detail the analysis and interpretation of user operation involved.

We describe a method that detects the user's intentions on the basis of the user's operations on a digital map and automatically retrieves and displays appropriate Web pages. Since the series of operations that a user performs on digital maps usually reflect specific intentions, the user's intentions can be detected by analyzing the operation sequences. We describe basic operation sequences for detecting intentions and present examples of Web page retrieval using these sequences. We also explain how queries are generated based on the detected intentions and how the retrieved Web pages are displayed. Furthermore, we describe the implementation issues of a prototype system based on our method. In our evaluation experiments, we can retrieve the desired web pages by analyzing user's map operations. Finally we discuss the results of experiments focusing on the defined operation sequences, the detected intentions, and the retrieved and displayed Web pages.

We propose a method that analyzes users’ search intentions by using their operations in a digital map search and automatically displays appropriate Web information. Although map operations, such as panning and zooming, is a search technique where new screen information is acquired, the information acquired in map applications is restricted. Therefore, we developed a system that searches Web pages based on users operations in digital maps and offers additional information. In the system, search intentions are analyzed by the relationships between chunk, which divide operation sequences into meaningful pieces. Our system generated queries searches and retrieved Web pages that are displayed to users based on their intentions.

Dynamic content integration of multiple information sources is one way of providing richer content that will satisfy the diverse demands of users. In this paper, we propose an XML-based language to compose synchronized content from Web and video content. The notable features of this language are as follows: (1) dynamic unit identification of content that is composed into synchronized content; and (2) dynamic retrieval of content through pre-defined retrieval criteria. This dynamic identification and retrieval of composable units are based on the author's intentions. Content authors can specify the units of their content that are to be integrated into new content by describing the conditions concerning this content and the conditions concerning the surrounding content. Although the proposed language looks like SMIL (synchronized multimedia integration language), it differs in its dynamic identification and retrieval capabilities. Indeed, the proposed language works just like the meta-mechanism for conventional SMIL. That is, the script written by the proposed language can generate SMIL data as its output.

Conventional Web bookmarks only contain URLs and titles of Web pages that users are interested in. This makes the process of remembering, sharing or ranking such pages difficult. The "context" of users' navigation can be described as collections of browsed pages. Conventional bookmarks do not contain such information. We believe that such context information conveys the users' intention and the importance of bookmarks. We introduce a notion of context-dependent Web bookmarks that reflects users' browsing histories. A context-dependent Web bookmark consists of (1) representative keywords of bookmarked pages and browsed pages, (2) the ranking value of bookmarked pages calculated by its context, as well as the URL and title of the page that the user bookmarked. Context-dependent bookmarks will make it possible for users to remember the situation of the bookmarking process, grasp the degree of significance of the bookmark, and share the bookmark among multiple users. Furthermore, it becomes possible to re-use context-dependent bookmarks as queries, which could be executed for unvisited Web pages. We also describe our Web browser prototype system based on the context-dependent bookmark function, and our experimental results.