Francesco Ricci

Multi-modal biometric systems, which leverage more than one sensor, are shown to be superior to uni-modal systems both in accuracy and robustness against attacks. In this paper, we present a novel behavioral multi-biometric user authentication framework that employs data fusion for biometric-based authentication. The proposed scheme extracts two types of data (Electromyography (EMG) and movements) from a single user’s clapping action type, via a worn smartwatch. During the user enrollment phase, the scheme creates a digital identity of the user based on the arm’s movement and EMG signatures generated through the entire period of clapping action. During the verification phase, the scheme authenticates the user based on the detected combined EMG and movement signature. The experimental analysis of the proposed framework employing a Deep Neural Network classifier demonstrates its efficacy …

With overtourism becoming an increasingly widespread problem, it is becoming more and more important to better analyse trends of tourists’ arrivals in a region, and to foresee the effects that alternative promotion campaigns, if delivered on an online destination platform, may have on the distribution of tourists in the region’s destinations. To facilitate this analysis, the study proposes a tool that enables a Destination Management Organization to simulate the effect of an online promotion campaign, which uses Recommender Systems techniques, to select which destinations are promoted to each tourist. A case study is developed in South Tyrol, a highly-visited province in the Italian Alps. In the simulated scenario, tourists, who visited the region in the past, are simulated to be exposed to promoted destinations. Each simulated tourist can choose an option among the tourist’s actual choice and other destinations that are …

Messaging apps, such as Telegram and WhatsApp, are routinely used to communicate, chat and make decisions. Group Recommender Systems (GRSs) have been introduced as self standing tools to support group interactions and decision-making. We present here a TelegramBot, named CHARM, that supports groups to make a decision on an arbitrary topic by leveraging GRSs techniques. CHARM helps elicit the group members’ preferences, ranks the items that the members have suggested to be considered, provides a summary of the current status of the discussion, and finally recommends a fair choice. A focus group study has revealed that the designed functionality includes features that users expect to find in a bot aimed at supporting group decision-making.

Nowadays, the users of a web platform, such as a video-on-demand service or an eCommerce site, are routinely using the platform’s recommender system (RS) when choosing which item to consume or buy (e.g. movies or books). It is therefore important to understand how the exposure to recommendations can influence the users’ choices, particularly the quality and distribution of the chosen items. However, users, even in the presence of the same RS, may show diverse and even atypical choice behaviours, which are independent of the RS; they may have a preference for choosing more popular or recent items. The effect of these behaviours on the collective evolution of the choices and the performance of the RS is not well-understood yet. In fact, in previous analyses, the users were supposed to only choose among the top recommendations, without any further discrimination. Hence, we first perform a correlation …

Group recommender systems (GRSs) identify items to recommend to a group of people by aggregating group members’ individual preferences into a group profile and selecting the items that have the largest score in the group profile. The GRS predicts that these recommendations would be chosen by the group by assuming that the group is applying the same preference aggregation strategy as the one adopted by the GRS. However, predicting the choice of a group is more complex since the GRS is not aware of the exact preference aggregation strategy that is going to be used by the group. To this end, the aim of this article is to validate the research hypothesis that, by using a machine learning approach and a dataset of observed group choices, it is possible to predict a group’s final choice better than by using a standard preference aggregation strategy. Inspired by the Decision Scheme theory, which first tried to …

Overtourism, i.e., the excessive presence of tourists in a location, is a widespread problem. To tame it, it is essential to understand tourism trends (tourists arrivals in the region areas) but also to predict the effects that personalised marketing campaigns, which are routinely performed by destination management organisations, may have on such trends. To facilitate this analysis, we propose a simulation system and we showcase its application to South Tyrol, a tourism region in the Italian Alps. The performed simulations are based on actual and feature-rich tourism arrivals data, collected in the past ten years. We simulate that the logged tourists are exposed to some advertised districts and their consequent choices. The demoed system enables the analysis of tourism data, the set up of marketing effect simulations, and the visualisation of their results. The system may reveal the broad effect of personalised and non …

BackgroundPrescriptive Process Monitoring systems aim at recommending, during the execution of a business process, interventions that, if followed, prevent poor performance of the process. Such interventions have to be (i) reliable: they have to guarantee the achievement of the desired outcome or performance and (ii) flexible: they cannot overturn the normal process execution.ProblemMost of the Prescriptive Process Monitoring solutions perform well in terms of recommendation reliability but provide the users with recommendations expressed in terms of specific activities that have to be executed without caring about their feasibility.MethodWe propose a new Outcome-Oriented Prescriptive Process Monitoring system recommending temporal relations among activities that have to be guaranteed during the process execution. The proposed system is based on a Machine Learning model that learns the …

We often make choices that involve a group of people, such as selecting a movie to watch with friends or choosing a travel destination to visit with the family. Sometimes, a single member of the group may be in charge of making the decision for the group, by playing the role of “organizer”. Although some tools for supporting Group Decision-Making have been proposed, none of them have considered the case where a single group member is autonomously making such a decision, hence entering the preferences of the group members, interacting with the system, and finally selecting a proper recommendation. In this paper, we introduce MyFoodGRS, a web application for a single user to find a proper restaurant for their group, that supports the previously mentioned tasks. We introduce an interaction design to follow the Attribute and Socially-based group decision patterns, and we report the positive result of the …