The emergence of Mobility as a Service (MaaS) highlights the need for personalized and exploration-oriented recommendation that account for users' subjective preferences and multisensory experiences. However, existing approaches lack real-world sensory and contextual data to support such personalization. To address this gap, we developed FreePOST, a location-based information collection system that enables users to submit photos together with multisensory and location impressions tied to specific places. In a field study spanning over three months in Kyoto, 69 participants contributed more than 8,000 posts. The collected data were visualized in real time via a web application with an interactive map interface. Because the sensory tags assigned to locations may depend not only on the characteristics of the place itself but also on the personality traits of the participants, we also collected personality and behavioral data using the Big Five Inventory and the Brief Sensation Seeking Scale. Our results demonstrate the ability to effectively capture Kyoto's sensory landscapes and context-aware experiential features, providing a valuable foundation for integrating subjective and multisensory data into future personalized search and recommendation in MaaS contexts. This demo will showcase FreePOST's core functions of multisensory data submission and interactive map visualization.

Web advertising services have exhibited consistent growth over the years. However, the conventional methods of web advertising recommendations, relying on keyword matching with search queries and browsing histories, encounter challenges when it comes to effectively targeting users with hidden or latent interests. In contrast, the use of mobile device location data in advertising recommendations often centers around physical store proximity. To address these limitations, our research aims to enhance web advertising recommendations by analyzing latent user interests through real-world behavioral data. This study specifically investigates the influence of area size on user behavioral analysis and its subsequent impact on the accuracy of predicting visit probabilities. We achieve this by extracting the user’s activity range from user behavior (movement) log data and geotagged tweets. Subsequently, we tally the places visited by the user, considering spot attributes, and convert this data into feature vectors. Utilizing these feature vectors in conjunction with various classification methods, we build learning models. In this paper, we present and evaluate these learning models employing different area sizes, verifying their accuracy in predicting user visits to specific stores.

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.

As an important foundation of the smart city, MaaS (Mobility as a Service) provides a seamless passenger experience using multiple modes of transport from the beginning until the end of a journey, and its developments have attracted keen interest from the industry in recent years [1] [2] . Furthermore, as the last mile of the MaaS, electric bicycle is an important and environmental-friendly transportation [3] [4] .

Advertisement (ad) recommendation services for mobile users are rapidly increasing. The conventional ways of recommending ads are based on the analysis of user's explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on a method to analyze the user's latent interest in web browsing history which categorized positive and negative behaviors. However, we think that the latent interest of users appears not only in the web space but also in the physical space. In this paper, we adapt the method of the linked pages to physical space locations using geo-tagged tweets. Based on several evaluations, we discuss the possibility to recommend ads according to the user's current location.

advertisement (ad) recommendation services for mobile users is rapidly increasing. The conventional ways of recommending ads are based on the analysis of users' explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on an analysis of the user's latent interest on web browsing history which categorized positive and negative behaviors. In this paper, we adapt the method of the linked pages to real world locations using geo-tagged tweets. By several evaluations, we discuss the possibility to recommend ads according to the user's current location.

In recent years, an evolutionary change occurred in the digital world when various social networking sites became popular. This led to a big increase of users who share their activities in digital form. A huge amount of digital information has become available, providing researchers with a unique insight into the behavior and activities of entire population. The use of Geo-tagged social media data became an emerging trend to represent user actions and behaviour at specific geographical locations. However, the inaccuracies of Geo-tagged data from social media often limits the utility of this data source in micro scale analysis (at the street or place of interest level (POI)). Our study supports how social media data could be matched accurately with specific physical locations. More specifically, our investigation includes Geo-tweet data and image data from flickr to map accurately physical location using machine leaning and deep learning techniques. In this paper, a preliminary discussion of this work is provided, using Geo-tagged data from Twitter and Open StreetMap in cities such as San Francisco and London as well as for image mapping using Flickr data in cities such as San Francisco and Kyoto.

Nowadays routing systems can provide optimal routes in terms of time and travel distance. However, they do not consider special needs of certain group of users. For example, people recovering from alcohol and drug addiction may want to travel a route that is alcohol and drug-free. In this demonstration, we propose a system we built that helps with this special need. We detect if a street is related to alcohol and drug by exploiting Web open data, including Foursquare, microblog tweets, Google Street View images, and crime data. We calculate an alcohol and drug relevance score using unsupervised methods, to be used in route ranking. Our system prototype is ready to be tested for the cities of San Francisco and Kyoto.

The vast and growing amount of publicly available real-time information from social network services such as Twitter could provide many benefits for improving public health and safety, especially towards the area of crime prevention. While prior studies have leveraged such data to help in the prediction of criminal incidents, we have developed a crime investigation tool which utilizes Twitter data to aid in crime analysis. The tool provides contextual information about crime incidents by visualizing the spatial and time-based characteristics of a crime and its context using data from nearby tweets and from the criminal history of a target place. In addition, sentiment analysis is also carried out with the identified tweets to further examine the negative characteristics of the spatial areas related to the different crimes in question. A demonstration prototype of this tool was developed as a web application for the area of San Francisco.

For a tourist who wishes to stroll in an unknown city, it is useful to have a recommendation of not just the shortest routes but also routes that are pleasant. This paper demonstrates a system that provides pleasant route recommendation. Currently, we focus on routes that have much green and bright views. The system measures pleasure scores by extracting colors or objects in Google Street View panorama images and re-ranks shortest paths in the order of the computed pleasure scores. The current prototype provides route recommendation for city areas in Tokyo, Kyoto and San Francisco.

This paper proposes a novel system which utilizes information from a social network services to suggest food venues to users based on crowd preferences. To recommend an appropriate food venue for each crowd preference, the system ranks food venues in each region by using an improved collaborative filtering method based on the differences between locations and languages in geo-tagged tweets. A key feature of the proposed system is the ability to suggest food venues in regions where very few geo-tagged tweets are available in a specific language by using the weighted similarity by others' preferences. To implement the system, more than 26 million tweets from European countries were collected and analyzed based on 6 languages and 7 regions. Afterwards, we provide an evaluation of the ranked venues proposed by the system based on 89 French speakers in 7 European countries.

Microblogging platforms such as Twitter have been recently frequently used for detecting real-time events. The spatial component, as reflected by user location, usually plays a key role in such systems. However, an often neglected source of spatial information are location mentions expressed in tweet contents. In this paper we demonstrate a novel visualization system for analyzing how Twitter users collectively talk about space and for uncovering correlations between geographical locations of Twitter users and the locations they tweet about. Our exploratory analysis is based on the development of a model of spatial information extraction and representation that allows building effective visual analytics framework for large scale datasets. We show visualization results based on half a year long dataset of Japanese tweets and a four months long collection of tweets from USA. The proposed system allows observing many space related aspects of tweet messages including the average scope of spatial attention of social media users and variances in spatial interest over time. The analytical framework we provide and the findings we outline can be valuable for scientists from diverse research areas and for any users interested in geographical and social aspects of shared online data.

Microblogging platforms such as Twitter have recently received much attention as great sources for live web sensing, real-time event detection and opinion analysis. Previous works usually assumed that tweets mainly describe "what's happening now". However, a large portion of tweets contains time expressions that refer to time frames within the past or the future. Such messages often reflect expectations or memories of social media users. In this work we investigate how microblogging users collectively refer to time. In particular, we analyze half a year long portion of Japanese and four months long collection of US tweets and we quantify collective temporal attention of users as well as other related temporal characteristics. This kind of knowledge is helpful in the context of growing interest for detection and prediction of important events within social media. The exploratory analysis we perform is possible thanks to the development of visual analytics framework for robust overview and easy detection of various regularities in the past and future-oriented thinking of Twitter users. We believe that the visualizations we provide and the findings we outline can be also valuable for sociologists and computer scientists to test and refine their models about time in natural language.

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 present a novel system that combines the advantages of social communication and Web search by simultaneously discovering important pages and users. First, the system provides a communication interface attached to pages, which allows users to talk with each other in real time while browsing the same page, i.e., page-centric communication. Then, the system can efficiently provide two ranking lists of pages and users by analyzing a hybrid structure of hyperlinks (page-page relationship) and social links (page-user relationship and user-user relationship). Thus, users can efficiently search for important pages as well as important users related to their queries through the ranking function, and immediately obtain useful information or knowledge from not only pages themselves but also from other users.

The abundance of content on the web and the lack of quality control require more refined approaches in analyzing online information. In this paper, we propose evaluating the extent to which web search results cover important and recent news related to real-world objects. Our method allows for identifying search results that provide comprehensive overviews of major events related to user queries or that contain most recent information.

Recently, along with the rapid growth of the Web, the preservation efforts have also increased. As a consequence, large amounts of past Web data are stored in Web archives. This historical data can be used for better understanding of long-term page topics and characteristics. In this paper, we propose an interactive visualization system called Page History Explorer for exploring page histories. It allows for roughly portraying evolution of pages and summarizing their content over time. We use a temporal term cloud as a structure for visualizing prevailing and active terms appearing on pages in the past.

We developed a mechanism for photographing people and annotating their behavior along with nearby elements using multiple embedded cameras. In addition, we developed a method of dynamically integrating and presenting the recorded content. As conventional cameras are used to photograph objects selected by users, taking pictures that include users while holding the camera is difficult. Security camera systems take pictures that include people and nearby elements, but such systems cannot show the intentions of the people being photographed. After detecting the intentions and behaviors of subjects with radio frequency identification (RFID) tags, our system selects the best camera from cameras located in an area, and then the camera photographs the subject and the surrounding area. In addition, these photos can be annotated with information about the context and movement history of the subject. We created a prototype of our system and determined its effectiveness experimentally.

We developed a mechanism for photographing people and annotating their behavior along with nearby elements using multiple embedded cameras. In addition, we developed a method of dynamically integrating and presenting the recorded content. As a conventional camera is used to photograph objects selected by a photographer, taking pictures that include him while holding the camera is difficult. Security camera systems take pictures that include people and nearby elements, but such systems cannot take the user aspect of intentions and interest of the people. After detecting the intentions and behaviors of subjects using radio frequency identification (RFID) tags, our system selects the best camera from cameras located in an area, and then the camera photographs the subject and the surrounding area. In addition, these photos can be annotated with meta datainformation about the context and interest of the subject.

We describe a browser for the past web. It can retrieve data from multiple past web resources and features a passive browsing style based on change detection and presentation. The browser shows past pages one by one along a time line. The parts that were changed between consecutive page versions are animated to reflect their deletion or insertion, thereby drawing the user's attention to them. The browser enables automatic skipping of changeless periods and filtered browsing based on user specified query.