Choosing what to eat on food delivery platforms can be a complex and often unsupported task, as current systems largely rely on menu browsing and offer limited support for preference articulation. In this paper, we present "Voglia di" (Italian for "craving for"), a food recommender system designed to support decision-making under preference uncertainty. The system guides users to articulate their momentary cravings by leveraging four key dimensions: cuisine type, flavor, desired mood, and dietary needs. To explore the effectiveness of the proposed solution, we conducted a preliminary within-subject user study (N = 28) that investigated the system’s ability to support users in satisfying their current cravings, as well as its impact on efficiency and overall quality of the food selection experience, in comparison to a commercial food delivery platform. These findings provide preliminary evidence of the potential of structured preference elicitation in the food domain, encouraging further investigation in real-world scenarios.

Travel experiences can challenge individuals with Autism Spectrum Disorder (ASD) before embarking on a trip, because of the complexity of planning, but also during the trip, due to sensory overstimulation while visiting places. Artificial Intelligence (AI) could help overcome some obstacles. However, it might represent a barrier for people with mid-functioning autism if their needs for assistance are not considered when designing tourism applications. Moreover, most trip planners fully control the generation of solutions, undermining users’ decision autonomy. To address these challenges, we explored the development of itinerary planning technologies through a human-centered approach. We aimed to balance guiding users in their decision-making with empowering them to plan tours independently. The result is CARES, an Artificial Intelligence-driven trip planner designed for autistic adults, which we present in this article. CARES applies a collaborative approach to developing itineraries, based on the exploitation of (i) AI technologies for information filtering and interactive itinerary planning that are robust to the data scarcity characterizing the autism domain; (ii) a user interface that reduces information overload and decision fatigue in information exploration. We tested CARES with 14 autistic adults, gathering insights to guide future design and improve the usability of Artificial Intelligence for neurodivergent users. The results indicate that our approach provides an accessible solution for enhancing the travel experiences of mid- to high-functioning autistic adults. In doing so, it contributes to more inclusive tourism. • We found that, despite difficulties, autistic adults wish to plan trips autonomously. • We propose a Human-Centered AI trip planner for mid/high-functioning ASD users. • Step-by-step guidance helps autistic users plan trips with limited effort. • The app empowers users to make choices while AI guides the process, ensuring balanced control. • The app balances user guidance and decision-making control.

Persuasive systems aim to support behavior change using interactive technologies. Among them, adaptive persuasive games, which combine game-based approaches with adaptive features, have recently emerged as promising tools for promoting positive behavioral changes. In this article, we report the findings of a systematic literature review of existing adaptive persuasive games (N = 55), following the Grounded Theory approach for Literature Review. We point out the main types of game-based approaches employed in the literature and the importance of aesthetics, playability, and variety in the game design. Then, we investigate the adaptation techniques employed and the central role of users’ individuality in the proposed designs. Moreover, we analyze the main persuasive theories on which adaptive persuasive games rely, as well as the most commonly used intervention designs. Finally, we describe the evaluation methodologies used and the outcomes of the proposed interventions in terms of behavior change, motivation and engagement. In conclusion, we discuss the issues emerging from the current literature and identify possible solutions and future research directions for adaptive persuasive game design.

Autism Spectrum Disorder (ASD) affects sensory perception, making spatial exploration difficult. Recommender systems can assist ASD users by suggesting Points of Interest (POIs) aligned with their sensory preferences. However, demographic constraints, difficulties in engaging ASD users, and the complexity of obtaining sensory data position POI recommendation for ASD people as a low-resource problem. In this paper, we identify key challenges in developing such systems and present our ongoing efforts. Using a local ASD center as a use case, we are developing a structured user involvement protocol. From the limited data, we are deriving knowledge graphs (KGs) to model preferences and sensory aspects. We are then exploring KG-based techniques to generate paths from users to POIs to suggest. With psychologists, we are refining the paths structure to match varying complexity levels and translate them into natural language accessible for people with ASD.

The Workshop on Information Retrieval for Understudied Users (IR4U2) serves as a platform to highlight information retrieval (IR) research that directly impacts often understudied user groups. The second (IR4U2) workshop focuses on a user-centred AI perspective, which is vital for informing the design, development, and assessment of information retrieval systems that thoughtfully address the diverse needs of understudied populations, ensuring genuine accessibility and inclusivity. The objectives of IR4U2 are: (1) to build community and awareness by sharing AI and IR developments that serve underrepresented user groups in this research area; (2) to identify challenges and open issues along with lessons learned and challenges inherent to this area of research; and (3) to spark discussions that establish common frameworks for future research.

In this paper, we describe an empirical evaluation of the user's perceived usefulness of recommendations for configuration tasks in a smart home scenario. Our results suggest that while overall recommendations help improve the performance in the task, the psychological traits of Self-efficacy and Need for Cognition play an important role in determining the perceived usefulness of the recommendations. These effects are somehow different from those reported in other studies where recommendations are provided for a choice task rather than a constructive task. Thus, our results offer evidence regarding the importance of incorporating the evaluation of personality traits into the design of configuration constructive tasks in order to make informed decisions on whether and how to provide users with recommendations. Additionally, they show that it is crucial to take into account the nature of the task and, where possible, individual competencies.

In this paper, we present an experiment where we study the impact of different types of explainable security warnings in a smart home environment. Results show that detailed informal-style explanations are evaluated more positively and are more effective in promoting safe home automation.

Users’ feedback is becoming more and more important in many different contexts of users’ interaction with computerised systems, such as in recommender systems, social network, e-democracy, quantifi...

This special issue highlights state-of-the-art research in intelligent systems and technology for people with diverse abilities. To control scope, we pay particular attention to cognitive diversity, including but not limited to, neurodevelopmental disorders and autism, cognitive and learningdisabilities, and dementia. We introduce the papers in this special issue by contextualizing them according to different research areas. By curating leading-edge contributions in this area, we aim to raise awareness about research challenges and requirements inherent in the development and assessment of systems designed for these categories of users.

The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior. Some questions that motivate this workshop are: What kind of theory is more suitable to inform the design of behavior change recommender systems? What kind of personal data (e.g., coming from environmental sensors, wearable devices, etc.) should we use to design behavior change recommendations? How should we deliver them (i.e., what kind of communication channels and interfaces should we use)? What kind of strategies should we implement to design timely and contextualized recommendations? How can we support the user's motivation to adhere to the recommendations provided? How can we “persuade” users in the long term?

Negative information plays an important role in the way we express our preferences and desires. However, it has not received the same attention as positive feedback in recommender systems. Here we show how negative user preferences can be exploited to generate recommendations. We rely on a logical semantics for the recommendation process introduced in a previous paper and this allows us to single out three main conceptual approaches, as well as a set of variations, for dealing with negative user preferences. The formal framework provides a common ground for analysis and comparison. In addition, we show how existing approaches to recommendation correspond to alternatives in our framework.

Nowadays, when dealing with Internet of Things (IoT) for people with no prior experience in programming or in designing technology, End-User Development (EUD) solutions offer wide and powerful approaches to support end-users in designing their own IoT smart things and systems. The main goal of this edition of the workshop is to encourage and stimulate a wealthy confrontation on heterogeneous topics related to EUD for IoT applications that exploits different interaction paradigms and innovative interface design. The outcome of the workshop are thought-provoking contributions that range from accessibility and security for IoT systems and devices up to personalization of smart objects.

Nowadays, most interactive social systems allow users to react to their contents and exploit user reactions to provide intelligent behaviours, such as adaptation or recommendation. Therefore, carefully understanding and designing the user/system dialogue that revolves around reaction provisioning is a crucial aspect. In this paper, we introduce the UpRISEmodel with the aim of formally describing the user/system interaction while providing and using reactions. Then, we show how this model can be used to formally represent and describe interactive social systems that collect user reactions, as well as to compare them. In addition, we exemplify how the UpRISEmodel can provide a sort of checklist that stimulates system designers to approach design/redesign tasks involving user reactions in a thorough and well-structured manner, suggesting all the possibly relevant points with respect to different usability and performance-related goals. This approach can be seen as the first step towards more transparency in the design of intelligent interactive systems.

In this paper we study whether visualizations based on color maps can encourage the intuitive interpretation of detailed descriptions, as the ones proposed in the formal model UPRISE, designed to analyze interactive system components, such as reaction tools, which allow users to provide reactions. We carried out a between-subjects experiment where 56 participants had to group 6 systems according to similarity using either color maps or textual descriptions. Results showed that color maps seem to favour inter-user agreement in comparison and grouping tasks.

The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model.

People with autism have idiosyncratic sensory experiences, which may impact on how they live the “spaces” of their everyday life. Starting from an investigation of their conception and experience of “secure places,” we defined a series of user requirements for designing technology that supports their everyday movements in the urban environment. On the basis of such requirements, we developed an interactive system that leverages crowdsourcing mechanisms to map places that are perceived as secure by the population with autism.

Mobile applications have a great potential in making everyday environments more accessible from the cognitive point of view, allowing neurodiverse people, such as individuals with autism, dementia, or ADHD, to gain independency and find continuous support. This workshop will discuss the main technological, methodological, theoretical and design issues that researchers and practitioners are facing when designing mobile devices and services for neurodiversity, exploring novel strategies to address them. In doing so, we want to focus on the neurodiverse people's idiosyncratic needs, also exploring ways for directly involving them in the design process.

This paper describes the preliminary results of a project aimed to support people with autism in finding city places that match their "sensorial" preferences and aversions. Through a participatory design approach, we designed an interactive map that collects sensorial data about the urban environment exploiting crowdsourcing mechanisms.

Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.

Thanks to the advancements in ubiquitous and wearable technologies, Personal Informatics (PI) systems can now reach a larger audience of users. However, it is not still clear whether this kind of tool can fit the needs of their daily lives. Our research aims at identifying specific barriers that may prevent the widespread adoption of PI and finding solutions to overcome them. We requested users without competence in self-tracking to use different PI instruments during their daily practices, identifying five user requirements by which to design novel PI tools. On such requirements, we developed a new system that can stimulate the use of these technologies, by enhancing the perceived benefits of collecting personal data. Then, we explored how naïve and experienced users differently explore their personal data in our system through a user trial. Results showed that the system was successful at helping individuals manage and interpret their own data, validated the usefulness of the requirements found and inspired three further design opportunities that could orient the design of future PI systems.

Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.

Consumption practices show a strong presence of crystallised social understandings, organising rules and permanent ways of acting that prevent individuals from changing towards more sustainable habits. Over the years, human–computer interaction research tried to help people engage in sustainable lifestyles promoting the health of the Earth. However, by favouring an individualistic and rationalistic approach to design, these attempts often lacked a deep understanding of how individuals are intertwined with social dynamics and organisational structures that might determine their actions. In this article, we aim at exploring novel solutions to support people’s sustainable habits, by focusing on their everyday purchases. Using an ethnographic method grounded in the social practices approach, we analyse the value that individuals ascribe to activities and objects that seem already addressed to sustainable consumption: the packaging-free purchasing practices. Starting from the insights gathered from this research, and leveraging the opportunities opened by the 3D printing technology, we design an interactive system with the aim to break the old buying routines and support the reuse of containers.

This brief introduction begins with an overview of the types of research that are relevant to the special issue on Big Personal Data in Interactive Intelligent Systems. The overarching question is: How can big personal data be collected, analyzed, and exploited so as to provide new or improved forms of interaction with intelligent systems, and what new issues have to be taken into account? The three articles accepted for the special issue are then characterized in terms of the concepts of this overview.

The paper presents the result on cross-representation mediation of user models in the context of movie recommendation. We analyze the possibility of initializing the user models for a content-based recommender starting from movie ratings provided by users in other social applications. We focus in particular on (i) an approach for inferring user model preferences from rating and (ii) the experimentation of several methods to solve the missing value problem exploiting community-based ratings. We tested different variations of the proposed approach exploiting a subset of the MovieLens 10M Dataset, computing rating predictions, and MAE.

This paper describes a novel methodology to evaluate a social media application in its formative phase of design. Taking advantage of the experiences developed in the Alternate Reality Games, we propose to insert game mechanics in the test setting of a formative evaluation of a prototypical social system. As a use case, we present the evaluation of WantEat, a prototypical social mobile application in the gastronomical domain. The evaluation highlighted how the gamification of a field trial can yield good results when evaluating social applications in prototypical status. From a methodological point of view, gamifying a field trial overcomes the cold start problem, caused by the absence of active communities, which can prevent the participation of users and therefore the collection of reliable data. Our experience showed that the gamification of a field evaluation is feasible and can likely increase the quantity of both browsing actions and social actions performed by users. Based on these results, we then are able to provide a set of guidelines to gamify the evaluation session of an interactive system.

This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users' interests and should thereby be taken into account in recommendation. We consider, in particular, three ‘additional’ features that are usually shown to users within social networking environments: reachability from the user location, the reputation of the event in the community and the participation of the user's friends. Our study is aimed at evaluating whether adding this information to the description of the event type and topic, and including in the user profile the information on the relevance of these factors, can improve our capability to predict the user's interest. We approached the problem by carrying out two surveys with users, who were asked to express their interest in a number of events. We then trained, by means of linear regression, a scoring function defined as a linear combination of the different factors, whose goal was to predict the user scores. We repeated this experiment under different hypotheses on the additional factors, in order to assess their relevance by comparing the predictive capabilities of the resulting functions. The compared results of our experiments show that additional factors, if properly weighted, can improve the prediction accuracy with an error reduction of 4.1%. The best results were obtained by combining content-based factors and additional factors in a proportion of ∼10:4.

The authors introduce new measures of semantic similarity and relatedness for ontological concepts, based on the properties associated to them. They consider two concepts similar if, for some properties they have in common, they also have the same values assigned to these properties. On the other hand, the authors consider two concepts related if they have the same values assigned to different properties. These measures are used in the propagation of user interest values in ontology-based user models to other similar or related concepts in the domain. The authors tested their algorithm in event recommendation domain and in recipe domain and showed that property-based propagation based on similarity outperforms the standard edge-based propagation. Adding relatedness as a criterion for propagation improves diversity without sacrificing accuracy. In addition, assigning a certain relevance to each property improves the accuracy of recommendation. Finally, the property-based spreading activation is effective for cross-domain recommendation.

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

The Social Web is now a successful reality with its quickly growing number of users and applications. Also the Semantic Web, which started with the objective of describing Web resources in a machine-processable way, is now outgrowing the research labs and is being massively exploited in many websites, incorporating high-quality user-generated content and semantic annotations. The primary goal of this special section is to showcase some recent research at the intersection of the Social Web and the Semantic Web that explores the benefits that adaptation and personalization have to offer in the Web of the future, the so-called Social Adaptive Semantic Web. We have selected two articles out of fourteen submissions based on the quality of the articles and we present the main lessons learned from the overall analysis of these submissions.

Wanteat is a framework and a suite of applications which allow users to interact with and explore mixed social networks of smart objects and people in the gastronomy domain, thus promoting the cultural heritage of a territory. Wanteat interaction model is based on the concept of a "wheel" [1].

This position paper describes the role ontologies can play in Mobile Context-Aware recommender systems. In a Semantic Web vision of recommender systems, the adoption of ontologies for modeling the domain, the context and the adaptation process can contribute to tailor the right information/service to users and thus facilitate the user-system interaction and the system communication with other agents.

Journal Article Adapting the interaction in a call centre system Get access Federica Cena, Federica Cena Department of Computer Sciences, University of Torino, Corso Svizzera 185, 10149 Torino, Italy Search for other works by this author on: Oxford Academic Google Scholar Ilaria Torre Ilaria Torre * Department of Computer Sciences, University of Torino, Corso Svizzera 185, 10149 Torino, Italy * Corresponding author. Address: Dipartimento di Informatica, università di Torino, Corso Svizzera 185, 10149 Torino, Italy. Tel.: +39 11 6706827; fax: +39 11 751603. E-mail addresses: [email protected] (F. Cena), [email protected] (I. Torre). Search for other works by this author on: Oxford Academic Google Scholar Interacting with Computers, Volume 18, Issue 3, May 2006, Pages 478–506, https://doi.org/10.1016/j.intcom.2005.11.007 Published: 04 January 2006

In this paper we described UbiquiTO, an agent-based system that acts as an expert tourist guide for mobile users, providing different information according to the device, the user and the context. The system uses feedbacks coming from localization to acquire the knowledge required to provide location-based services and to update the user model.

This paper describes the innovative combination of speech recognition and personalized response generation with the adaptive routing of calls to the operator which best fits the caller's features. The project aims at supporting the user incrementally, starting from a personalized automatic support and moving to a proficient human one, when it is needed. In particular the paper shows the adaptive workflow of the answering process and focuses on the principles for providing the personalized speech response.