Francesco Ricci

More than twenty-five years ago, first ideas were developed on how to design a system that can provide recommendations to groups of users instead of individual users. Since then, a rich variety of algorithmic proposals were published, e.g., on how to acquire individual preferences, how to aggregate them, and how to generate recommendations for grou...

Group Recommender Systems (GRSs) have been studied and developed for more than twenty years. However, their application and usage has not grown. They can even be labeled as failures, if compared to the very successful and common recommender systems (RSs) used on all the major ecommerce and social platforms. As a result, the RSs that we all use now,...

Group recommender systems (GRSs) support groups of users to find items, e.g., restaurants, that suit, as much as possible, all the group members’ preferences. We consider a GRS scenario where a single member of the group, the organizer, uses the system to find and choose a suitable restaurant for the entire group. We present a novel GRS that helps...

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 fa...

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 sa...

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 ty...

Background: Prescriptive 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 overtur...

Group recommender systems (GRS) identify items to recommend to a group by aggregating group members' individual preferences into a group profile. The preference aggregation strategy used to build the group profile can also be used for predicting the item that a group may decide to choose, i.e., by assuming that the group is applying exactly that st...

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 qu...

In this paper we propose a gesture-based user-friendly smartwatch-based user authentication scheme called ClapAuth to authenticate the users to gain physical access to a secure infrastructure. In ClapAuth users are authenticated by performing clapping actions, while wearing their smartwatch in one hand. ClapAuth, while users perform clapping gestur...

Powder Metallurgy (PM) is the branch of Metallurgy that deals with the design/production of near net-shaped sintered workpieces with different shapes and characteristics. The produced sintered workpieces are used in automotive, aviation, and aerospace industries, just to name a few. The quality of the produced sintered workpieces largely depends on...

Prescriptive Process Monitoring systems recommend, during the execution of a business process, interventions that, if followed, prevent a negative outcome of the process. Such interventions have to be reliable, that is, they have to guarantee the achievement of the desired outcome or performance, and they have to be flexible, that is, they have to...

In this paper we propose a gesture-based user-friendly smartwatch-based user authentication scheme called ClapAuth to authenticate the users to gain physical access to a secure infrastructure. In ClapAuth users are authenticated by performing clapping actions, while wearing their smartwatch in one hand. ClapAuth, while users perform clapping gestur...

The rise of process data availability has recently led to the development of data-driven learning approaches. However, most of these approaches restrict the use of the learned model to predict the future of ongoing process executions. The goal of this paper is moving a step forward and leveraging available data to learning to act, by supporting use...

Recommender systems (RSs) are personalized information search and discovery applications helping users to identify and choose useful items and information. In this paper, we focus on the tourism application scenario and its specific requirements. We discuss a novel RS approach that copes with the specific application constraints of the domain and p...

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RSs are indeed often capable to make astonishingly good suggestions. With countless papers being...

The rise of process data availability has led in the last decade to the development of several data-driven learning approaches. However, most of these approaches limit themselves to use the learned model to predict the future of ongoing process executions. The goal of this paper is moving a step forward and leveraging data with the purpose of learn...

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 exposure to recommendations can influence the users' choices, particularly the quali...

Climbing is a popular sport for active tourists and recreational sportsmen. Alpine climbing areas, such as the Alps, can attract tourists from all over the world. Various websites, mobile applications, and books are used by climbers to obtain information on important aspects of the available climbing routes, including their properties, location, an...

Recommender systems’ (RSs) research has mostly focused on algorithms aimed at improving platform owners’ revenues and user’s satisfaction. However, RSs have additional effects, which are related to their impact on users’ choices. In order to avoid an undesired system behaviour and anticipate the effects of an RS, the literature suggests employing s...

Recommender Systems (RSs) research has become interested in assessing the system effect on the actual choices of their users: the distribution and the quality of the choices. By simulating users’ choices, influenced by RSs, it was shown that algorithmic biases, such as the tendency to recommend popular items, are transferred to the users’ choices.I...

In the past decades, a number of technological developments made it possible to continuously collect various types of sport activity data in an unobtrusive way. Machine learning and analytical methods have been applied to flows of sensor data to predict the conducted sport activity as well as to calculate key performance indicators. In that scenari...

This article focuses on sport climbing and on the design of innovative tools for supporting climbers to browse and search routes to climb. The difficulty of a route, its grade, is normally assessed by expert climbers, named route setters. A regular climber, after trying a route, may perceive it more or less difficult than the route setter. It is im...

Recommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user’s ratings or choices. But, when a precise RS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can co...

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the l...

Online platforms which assist users in finding a suitable match, such as online-dating and job recruiting environments, have become increasingly popular in the last decade. Many of these environments include recommender systems which, for instance in online dating, aim at helping users to discover a suitable partner who will likely be interested in...

Behavioral biometric-based smartphone user authentication schemes based on touch/swipe have shown to provide the desired usability. However, their accuracy is not yet considered up to the mark. This is primarily due to the lack of a sufficient number of training samples , e.g., swiping gestures: users are reluctant to provide many. Consequently, th...

We consider the urban tourism scenario, which is characterized by limited availability of information about individuals’ past behaviour. Our system goal is to identify relevant next Points of Interest (POIs) recommendations. We propose a technique that addresses the domain requirements by using clusters of users’ visits trajectories that show simil...

Sport climbing is becoming an increasingly popular competitivesport as well as a recreational activity. For this reason, indoor sportclimbing operators are constantly trying to improve their servicesand optimally use their infrastructure. One way to support such atask is to track the climbing activities performed by visitors whileclimbing. This pap...

The major focus of recommender systems (RSs) research is on improving the goodness of the generated recommendations. Less attention has been dedicated to understand the effect of an RS on the actual users' choices. Hence, in this paper, we propose a novel simulation model of users' choices under the influence of an RS. The model leverages real rati...

Software developers’ knowledge of integrated development environment (IDE) directly impacts on their productivity. IDE command recommender systems aim at identifying and convincingly presenting to software developers functionality that can help them to accomplish their daily tasks, without overloading them with well known or useless information. Co...

An individual’s conflict resolution styles can have a large impact on the decision making process of a group. This impact is affected by a variety of factors, such as the group size, the similarity of the group members, and the type of support offered by the recommender system, if the group is using one. Measuring the effect of these factors goes b...

Group recommender systems generate recommendations for a group by aggregating individual members’ preferences and finding items that are liked by most of the members. In this paper we introduce a new approach to preference aggregation and group choice prediction that is based on a new form of weighting individuals’ preferences. The approach is base...

Media monitoring services allow their customers, mostly companies, to receive, on a daily basis, a list of documents from mass media that discuss topics relevant to the company. However, media monitoring services often generate these lists by using keyword-filtering techniques, which introduce many false positives. Hence, before the end users, i.e....

In this paper we introduce a novel model for simulating the choice making procedure of users under the influence of a Recommender System (RS). Our model leverages the knowledge of users' preferences and simulates repeated choices. We investigate the evolution of these simulated choices in the presence of different RSs and analyse their impact on th...

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), man...

User preferences in the form of absolute feedback, s.a., ratings, are widely exploited in Recommender Systems (RSs). Recent research has explored the usage of preferences expressed with pairwise comparisons, which signal relative feedback. It has been shown that pairwise comparisons can be effectively combined with ratings, but, it is important to...

Users of a recommender system may be requested to express their preferences about items either with evaluations of items (e.g. a rating) or with comparisons of item pairs. In this work we focus on the acquisition of pairwise preferences in the music domain. Asking the user to explicitly compare music, i.e., which, among two listened tracks, is pref...

The Internet of Things (IoT) enables new ways for exploiting the synergy between the physical and the digital world and therefore promises a more direct and active interaction between tourists and local products and places. In this article we show how, by distributing sensors/actuators in the environment or attaching them to objects, one can sense,...

Online platforms which assist people in finding a suitable partner or match, such as online dating and job recruiting environments, have become increasingly popular in the last decade. Many of these platforms include recommender systems which aim at helping users discover other people who will also be interested in them. These recommender systems b...

Preference data in the form of ratings or likes for items are widely used in many Recommender Systems. However, previous research has shown that even item comparisons, which generate pairwise preference data, can be used to model user preferences. Moreover, pairwise preferences can be effectively combined with ratings to compute recommendations. In...

Recommender Systems (RSs) are commonly used in web applications to support users in finding items of their interest. In this paper we propose a novel RS approach that supports human decision making by leveraging data acquired in the physical world. We consider a scenario in which users' choices to visit points of interests (POIs) are tracked and us...

In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolutio...

Development tools have an impact on software engineers' productivity and quality of software construction. We believe that it is crucial to teach future software engineers how to exploit integrated development environment functionality, if we want to encourage the effective application of software development principles and practices. Our research...

Children, nowadays, are great consumers of media for them [6], and there is growing interest towards novel mechanisms that can consider their specific needs and improve both the recommendation process and output of videos for them. Children, in fact, have unique characteristics , which change with age. In particular, in the 8-12 age range, they lik...

Preprint of a book chapter published in Book Collaborative Recommendations Algorithms, Practical Challenges and Applications [https://doi.org/10.1142/11131] © [copyright World Scientific Publishing Company] [https://www.worldscientific.com/worldscibooks/10.1142/11131]

A major challenge for conversational group recommender systems is how to properly exploit the user's preferences induced by the interactions between group members, which may deviate from the user's long-term ones. We argue that the relative importance of the long-term and group-induced preferences should vary according to the specific group setting...

Group recommender systems are information filtering and decision support applications that are aimed at aiding a group of users in making decisions when they are considering a set of alternatives. State of the art solutions aggregate users’ preferences acquired before the actual decision making process and suggest items that fit the aggregated mode...

In this paper we present a demo application aimed at supporting the research in the field of tourism and mobility support in IoT augmented areas. The application collects tourists' choices while browsing Points of Interest (POIs) descriptions through a map-based interface that simulates user movement between POIs. Collected observations serve two p...

Life Meter is a health mobile application that helps users raise awareness of their quality of life by showing indicators derived from life-logs collected by commercial wearable trackers, smartphone sensors, and manual input. We describe the general infrastructure we developed for the collection and fusion of life-logs, how the quality of life indi...

Integrated development environments (IDEs) are complex applications that integrate multiple tools for creating and manipulating software project artifacts. To improve users' knowledge and the effectiveness of usage of the available functionality, the inclusion of recommender systems into IDEs has been proposed. We present a novel IDE command recomm...

We present a research tool for user preference elicitation that collects both explicit user feedback and unobtrusively acquired facial expressions. The concrete implementation is a web-based user interface where the user is presented with two music excerpts. After listening to both, the user provides a pairwise score (i.e. which of the two items is...

Obesity is a complex disease that involves genetic factors, inflammatory patterns, resilience and psycho-social factors. An effective system which is able to recommend adequate training for obese subjects that starts a new protocol would enhance the quality and success of the rehabilitation of these subjects. This paper presents a case-based reason...

In this abstract, we discuss how long-term and discussion-generated preferences can be appropriately combined in supporting group decision making. We measure the quality of a group recommendation model by varying the importance given to these two types of preferences in different group scenarios, where the group setting may impact on user's behavio...

Traditional food recommender systems exploit items' ratings and descriptions in order to generate relevant recommendations for the users. While this data is important, it might not entirely capture the true users' preferences. In this paper, we analyse the performance of a food recommender that allows users to enter their preferences in the form of...

Many Recommender Systems (RSs) rely on user preference data in the form of ratings or likes for items. Previous research has shown that item comparisons can also be effectively used to model user preferences and build RS. However, users often express their preferences by referring to specific features of the items. For instance, a user may like Ita...

Recommender systems generate recommendations by analysing which items the user consumes or likes. Moreover, in many scenarios, e.g., when a user is visiting an exhibition or a city, users are faced with a sequence of decisions, and the recommender should therefore suggest, at each decision step, a set of viable recommendations (attractions). In the...

In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources wi...

Implicit feedback recommender systems provide personalized suggestions for items that are predicted to be of interest to the user, by collecting online users' activity and inferring from it users' preferences. While the state-of-the-art models are built by employing observations of user actions of one single type, the usage of multiple action types...

Many recommender systems collect online users' activity and infer from it users' preferences. They record user actions of various types (e.g. clicks, views), and predict unknown, possibly future, interactions between users and items, mostly using Collaborative Filtering (CF) or Sequence Mining (SM) techniques. While both techniques have their advan...

Group recommender systems (GRSs) support users in a group when facing decision making tasks. Many GRSs simply perform a one shot aggregation of the individual preferences and then compute recommendations. In this paper, we propose a different approach; it allows users to iteratively express and revise their preferences during the decision making pr...

This paper describes a novel design of a graphical user interface (GUI) to recommend useful command within an integrated development environment. The recommendation GUI contains a description of the suggested command, an explanation why the command is recommended, and a command usage example. The proposed design is based on the analysis of relevant...

Context-aware recommender systems are information filtering and decision support applications that generate recommendations by exploiting context-dependent user preference data, such as ratings augmented with the description of the contextual situation detected when the user experienced the item. In fact, many contextual factors (e.g., weather, sea...

Group recommender systems aim at supporting a group of users in making decisions when considering a set of alternatives. State of the art solutions aggregate individual preferences acquired before the actual decision making process and suggest items that fit the aggregated model. In this paper, we illustrate a different approach, which is implement...

In this article we argue that the research on group recom-mender systems must look more carefully at group dynamics in decision making in order to produce technologies that will be truly beneficial for users. Hence, we illustrate a user study method aimed at observing and measuring the evolution of user preferences and actions in a tourism decision...

The TripAdvisor dataset is a dataset that we crawled from the TripAdvisor website. It contains ratings for POIs in the South Tyrol region of Italy that are tagged with contextual situations described by the conjunction of contextual conditions coming from three contextual factors, namely, type (e.g., couple, family or business trip), month (e.g., J...

Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techn...

Many recommendation techniques rely on the knowledge of preferences data in the form of ratings for items. In this paper, we focus on pairwise preferences as an alternative way for acquiring user preferences and building recommendations. In our scenario, users provide pairwise preference scores for a set of item pairs, indicating how much one item...

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed...

Context, as modeled through variables called contextual factors, can improve human-computer interaction. To date, in applications supporting software development, such as integrated development environments (IDEs) and recommendation systems for software engineering (RSSEs), contextual factors have generally been constrained to project artifacts, su...

Music and places can both trigger emotional responses in people. This chapter presents a technical approach that exploits the congruence of emotions raised by music and places to identify music tracks that match a place of interest (POI). Such technique can be used in location-aware music recommendation services. For instance, a mobile city guide m...

Many collaborative filtering recommender systems collect and use users' explicitly entered preferences in the form of ratings for items. However, in many real world scenarios, this form of feedback can be difficult to obtain or unavailable (e.g., news portals). In this case recommendations must be built by leveraging more abundant implicit feedback...

Many recommender systems rely on item ratings to predict users' preferences and generate recommendations. However, users often express preferences by referring to features of the items, e.g., "I like Tarantino's movies". But, it has been shown that user models based on feature preferences may lead to wrong recommendations. In this paper we cope wit...

The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which...

The software engineering community is continuously producing tools to tackle software construction problems. This paper presents a research study to identify which tools, artifacts, and commands developers use during task solving and how one can design software that can suggest and convince the developer to use specific software construction techni...