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.

When users engage in online shopping, they often rely on product reviews as a reference. However, efficiently determining the overall evaluation of each product from a large number of reviews is not easy. Previous studies have addressed this issue by automatically scoring each product based on text analysis of product reviews. On the other hand, evaluating the performance of the retail store itself poses challenges. There are usually fewer reviews specifically targeting the store, and evaluations of the store are often embedded within product reviews. As a result, automatically scoring the performance of retail stores is not a straightforward task. Therefore, in this study, we propose a method to extract evaluations of retail stores from product reviews and automatically score them based on store-specific criteria. Additionally, we developed a system that utilizes the calculated store scores, allowing users engaged in online shopping to search for the evaluations of the retail stores that sell the products they are interested in. We present the details of our proposed method and the development of the search system, along with the results of the evaluation experiments conducted using the developed system.

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] .

In recent years, there are various review sites on the Web. In online shopping, review sites are important because they strongly influence consumers’ purchasing decisions. We focus on cosmetic reviews and consider the skin type and usability of individual users. In order to realize the cosmetic recommendation system, we are working on the development of a review recommendation method by an automatic scoring system using an evaluative expression dictionary for each cosmetic item classification. Since cosmetic items have detailed classifications, it is necessary to build an evaluative expression dictionary for each cosmetic classification in order to perform automatic scoring. Therefore, it is desirable that the evaluative expression dictionary construction method be efficient and semi-automatic. In this paper, we try to improve the evaluative expression dictionary and examine the efficient method for developing evaluative expression dictionary based on Word2Vec for cosmetics.