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.

Most pedestrian navigation systems prioritize efficiency, which often directs users along the shortest route at the cost of serendipitous discovery and enjoyment. Recent efforts have explored roaming-supportive navigation, which encourages exploration by offering diverse, non-shortest routes, but its real-world impact on user experience remains underexplored. This paper presents an in-the-wild field study conducted in Asakusa, Tokyo, where participants followed either the shortest or exploratory walking routes guided in real-time by a Wizard-of-Oz prototype that enables interactive, turn-by-turn voice prompts despite urban GPS drift. We measured emotional and experiential responses using the Positive and Negative Affect Schedule (PANAS) and the Satisfaction with Travel Scale (STS). We also observed photo-taking behavior as an indicator of engagement. Our results show that participants on exploratory routes reported greater travel satisfaction, higher positive affect, and greater visual diversity in photo-taking behavior. These findings demonstrate that pedestrian navigation systems can go beyond efficiency to support emotionally enriching and cognitively meaningful mobility in everyday urban settings.

Itinerary planning is complicated for travelers because the traveling content, including places to visit, acceptable times, and distances, can be diverse. Travel recommender systems (TRSs) recommend the most relevant itineraries for a traveler. In this paper, we propose an interactive framework that allows users to edit itineraries directly on a map to suit their preferences better. The proposed framework collects feedback data by recording user itinerary modifications, infers positive and negative preferences, and fine-tunes the recommender models with our ranking-based loss function. This interaction-based data augmentation approach addresses data sparsity issues due to personalization by capturing a variety of travel item combinations. In our experiments, we evaluate multiple combinations of models and itinerary generation methods to show the effectiveness of integrating interaction data into TRSs. Our experimental evaluations demonstrate that our interactive TRS can provide itineraries that align with users’ preferences more in terms of point-set-wise and rank-wise accuracy; the integration consistently improves the accuracy for all combinations of the components, and particularly, the improvement is large for small backbone models.

In recommender systems, explanations are essential for supporting users’ decision-making processes. While many studies have focused on explanation content or user interface, the expression of textual explanations has been largely overlooked. The expression refers to textual styles such as formal or humorous, which we call tone in this article. Although tone contributes to smooth human communication, its impact on users’ perceptions of recommender systems remains largely unexplored. In particular, it is unclear whether the perceived effects of explanation tone differ by domain or user attributes. Therefore, we investigate the effects of explanation tones through two online user studies considering domains and user attributes. In the first study with 470 participants, we generated datasets using a large language model to create fictional items and explanations with six tones across three domains: movies, hotels, and home products. The participants evaluated two explanations for an item, each presented in a different tone, and rated 10 metrics. In the second study with 103 participants, we used a real-world dataset from the hotel domain and incorporated a simple personalized recommender system to examine effects of tone in a more realistic setting. The results revealed that the perceived effects of tones differ by domain and are significantly influenced by user attributes such as age and personality traits. Our findings suggest that appropriately adjusting the tone of explanations according to domains and user attributes can enhance the perceived effects of recommender systems.

In recommender systems, the presentation of explanations plays a crucial role in supporting users’ decision-making processes. Although numerous existing studies have focused on the effects (e.g., transparency) of explanation content, explanation expression is largely overlooked. Tone, such as formal and humorous, is directly linked to expressiveness and is an important element in human communication. However, studies on the impact of tone on explanations within the context of recommender systems are insufficient. Therefore, this study investigates the tonal effects of explanations through an online user study. We focus on a hotel domain and six types of tones. The collected data analysis reveals that the tone of explanations influences the perceived effects, such as trust and effectiveness, of recommender systems. Our findings suggest that the tone of explanations can enhance user experience in recommender systems.

Navigation has become an indispensable technology, especially when exploring unfamiliar environments. Traditional shortest route-based navigation focuses on route effectiveness, which may deprive users of the opportunity to explore new areas. To mitigate the difficulty of exploring environments and enrich our walking activities by stimulating our natural tendency to explore, we study a system utilizing alternative non-shortest diverse routes. In this paper, we explain the concepts of our roaming navigation and show how users can travel with our system to demonstrate the effect of our new navigation service for pedestrians.

We consider a general task called partial Wasserstein covering with the goal of providing information on what patterns are not being taken into account in a dataset (e.g., dataset used during development) compared to another (e.g., dataset obtained from actual applications). We model this task as a discrete optimization problem with partial Wasserstein divergence as an objective function. Although this problem is NP-hard, we prove that it satisfies the submodular property, allowing us to use a greedy algorithm with a 0.63 approximation. However, the greedy algorithm is still inefficient because it requires solving linear programming for each objective function evaluation. To overcome this inefficiency, we propose quasi-greedy algorithms, which consist of a series of techniques for acceleration such as sensitivity analysis based on strong duality and the so-called C-transform in the optimal transport field. Experimentally, we demonstrate that we can efficiently fill in the gaps between the two datasets, and find missing scene in real driving scene datasets.

Combinatorial optimization problems are ubiquitous for decision making in planning social infrastructures. In real-world scenarios, a decision-maker needs to solve his/her problem iteratively until he/she satisfies solutions, but such an iterative process remains challenging. This paper studies a new explainable framework, particularly for finding meeting points, which is a key optimization problem for designing facility locations. Our framework automatically fills the gap between its input instance and instances from which a user could obtain the desired outcome, where computed solutions are judged by the user. The framework also provides users with explanations, representing the difference of instances for deeply understanding the process and its inside. Explanations are clues for users to understand their situation and implement suggested results in practice (e.g., designing a coupon for free travel). We experimentally demonstrate that our search-based framework is promising to solve instances with generating explanations in a sequential decision-making process.

Cooperation among different vehicles is a promising concept for route planning of Mobility as a Service (MaaS). For instance, vehicle platooning on highways decreases fuel consumption because it reduces the air resistance and several trucks cooperate with each other when planning. Traditional platooning, however, cannot model cooperation among different types of vehicles because it assumes the homogeneity of vehicle types. We study a model that permits heterogeneous cooperation and discuss a route optimization problem under assumption that the heterogeneous cooperation benefits the objective function. We experimentally evaluate the formulation through using synthetic and real graphs based on a modern integer programming solver with various parameter settings, which are not tried in previous studies. We also compare the results by the solves with simple heuristic method developed in this paper and discuss the results to reveal the properties of the optimization problem with heterogeneous vehicle types.

Cooperation among different vehicles is a promising concept for Mobility as a Service (MaaS). A principal problem in MaaS is optimizing the vehicle routes to reduce the total travel cost with cooperation. For example, we know that platooning among large trucks could reduce the fuel cost because it decreases the air resistance. Traditional platoons, however, cannot model cooperation among different types of vehicles because the model assumes the homogeneity of vehicle types. We then propose a model that permits heterogeneous cooperation. Targets of our model include a logistic scenario, where a truck for the long-distance delivery also carries small self-driving vehicles for the last mile delivery. For those purposes, we formalize a new route optimization problem with heterogeneous cooperation, and provide its integer programming (IP) formulation as an exact solver. We evaluate our formulation through numerical experiments using synthetic and real graphs. We also validate our concept of heterogeneous cooperation for MaaS with examples.