This paper presents an experiment on the effects of inter-agents emotional alignment, a prerequisite for empathic communication, in Human-Robot Interaction (HRI). We describe a pipeline built around the Pepper robot with the idea of verifying the effect of emotionally-aligned communication toward a user. In particular, our goal is twofold, in that we investigate if and to what extent an emotionally-aligned, empathic dialogue impacts on (i) the communication style of the user, and (ii) the persuasive effectiveness of the robot, intended as its ability to alter or reinforce its interlocutors' attitudes and beliefs about the conversation topic. Both these aspects have been assessed in a controlled experiment with 46 participants, comparing a condition where the robot addresses participants with emotionally neutral sentences with a condition where the robot provides answers tailored to the emotions expressed in participants' input utterances. Results show how emotion alignment acts as an effective trigger for the elicitation of different communication styles of the users but also that, contrary to what we expected, it does not play any persuasive effect.

The paper presents an experiment on the effects of adaptive emotional alignment between agents, considered a prerequisite for empathic communication, in Human-Robot Interaction (HRI). Using the NAO robot, we investigate the impact of an emotionally aligned, empathic, dialogue on these aspects: (i) the robot’s persuasive effectiveness, (ii) the user’s communication style, and (iii) the attribution of mental states and empathy to the robot. In an experiment with 42 participants, two conditions were compared: one with neutral communication and another where the robot provided responses adapted to the emotions expressed by the users. The results show that emotional alignment does not influence users’ communication styles or have a persuasive effect. However, it significantly influences attribution of mental states to the robot and its perceived empathy.

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.

This paper presents an empirical evaluation of mid-air gestures in a web setting. Fifty-six (56) HCI students were divided into 16 groups and involved as designers. Then, they proposed a set of mid-air gestures to carry out the identified actions: 99 different mid-air gestures for 16 different web actions were produced in total. Designers validated their proposals involving external subjects, namely 248 users in total. Finally, we analyzed their results and identified the most recurring or intuitive gestures as well as the potential criticalities associated with their proposals.

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...

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, 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.

In this paper we present the results of two experimental evaluations carried out in the broader context of a research project on Industry 4.0. We envision in large displays operated through gestural interaction one of the key technologies for enabling real-time management of production processes and supporting decision-making. We developed prototype applications with the twofold aim of: (1) designing a vocabulary of one-handed, touchless gestures meant for interaction in a CAD-like environment, and (2) investigating the role of the cursor feedback. Our results provide a basis for the development of a new integrated wearable sensor which will be assessed with industry workers and in a more realistic setting, for better ecological validity.

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.

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.

Recommender systems support users in finding the appropriate information at the correct time. While traditional recommenders only take into account the stable preferences of target users (ego-based interests), some recent approaches in the area of social recommender systems have started to acknowledge that the mere fact of taking part in social relationships may cause individuals to modify their attitudes and behaviors, and have proposed methods for generating recommendations based on the preferences of the target users ’ social networks (network-based interests). However, little work has investigated how to effectively merge ego- and network-based interests. In this paper, we present SoNARS++, an advanced social algorithm that assesses the interest for an item to be recommended by combining a user’s personal interests for that item with the interests for that item of the user’s social network, depending on its structure and on social influence relationships among users. The results of the experimental evaluation we carried out show that SoNARS++ is comparable for its precision to a mainstream algorithm such as collaborative filtering but appears to provide users with more useful recommendations. RESEARCH HIGHLIGHTS • SoNARS++ as advanced social algorithm to generate recommendations for users. • It combines user personal preferences and preferences of the user social network. • It has comparable precision to collaborative filtering, but higher usefulness.

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.

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].