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Multimedia item characteristics are used in domains, such as recommender systems and information retrieval. In this work we distinguish two main groups of item characteristics: (i) item-centric item characteristic (ICIC) and (ii) user-centric item characteristic (UCIC). With the term ICIC we denote a characteristic of an item that (a) has roots in the item and (b) has the same value for all users, for example, the duration of a song. With the term UCIC, we denote a characteristic of an item that (a) has roots in the perception of the user from an item characteristic and (b) exhibits some variance across different users, for example, the perceived emotion of a song. We survey recent work that covers various types of UCIC, acquisition methods of UCIC, and domain usage of UCIC. We identify gaps in the research and provide guidelines for future work. [ABSTRACT FROM AUTHOR]

In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a location-based mobile service, a mobile phone text messaging service providing next word prediction, or a face image classification system. The main issue is that, often, data are collected, transferred, and processed by third parties. These transactions violate new regulations, such as GDPR. Furthermore, users usually are not willing to share private data such as their visited locations, the text messages they wrote, or the photo they took with a third party. On the other hand, users appreciate services that work based on their behaviors and preferences. In order to address these issues, Federated Learning (FL) has been recently proposed as a means to build ML models based on private datasets distributed over a large number of clients, while preventing data leakage. A federation of users is asked to train a same global model on their private data, while a central coordinating server receives locally computed updates by clients and aggregate them to obtain a better global model, without the need to use clients' actual data. In this work, we extend the FL approach by pushing forward the state-of-the-art approaches in the aggregation step of FL, which we deem crucial for building a high-quality global model. Specifically, we propose an approach that takes into account a suite of client-specific criteria that constitute the basis for assigning a score to each client based on a priority of criteria defined by the service provider. Extensive experiments on two publicly available datasets indicate the merits of the proposed approach compared to standard FL baseline. [ABSTRACT FROM AUTHOR]

When a person visits an unknown large city having multiple interesting locations, it is not so easy for him to find one location that is lively and convenient to visit in a given time-frame. To overcome such a problem, this paper proposes to make use of two technologies: smartphones, equipped with sensors for reading GPS coordinates; and multi-agent systems, providing assistance to users and gathering collective knowledge. Data collected by means of devices are analysed and organised in such a way to find locations that could be of immediate interest to people. Proposed agents gather opinions from several users, in terms of scores quantifying the level of satisfaction on visiting some place on a given time-frame. While gathering such an opinion, a solution is put into place to preserve user privacy (his location). Suggestions are made to potentially interested users by selecting for them locations according to closeness and satisfaction scores. In this approach, interesting locations emerge from the analysis of data gathered, hence scores and suggestions can be available for any large city in any place, provided that enough people hand data to the system. Moreover, such places are found dynamically according to people behaviour and preferences. [ABSTRACT FROM AUTHOR]

In the last decades, individual urban traffic flows have increased all over the world with a consequent growth of road congestion and environmental pollution. In this context, car-pooling is an interesting car-based alternative to satisfy the individual mobility demand by optimizing the car loading factor with respect to the number of passengers, provided that all the participants share trip origin and destination in the same time slot. To make the system more appealing, this paper proposes an on-demand car-pooling service adopting variable fares, on the basis of trip length and number of participants. Multi-agent, reputation and recommender system technologies in synergy with a routing algorithm have been used to this aim. Experiments on simulated data proved the potentiality of the proposed approach. [ABSTRACT FROM AUTHOR]

The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to support and guide them in their exploration tasks. Personalization mechanisms and recommender systems limit serendipitous encounters by selectively guessing the next item to show to users and potentially leading them into so-called filter bubbles. In the ideal case, these recommendations, except of being accurate, should be also novel. However, up to now most platforms fail to provide both novel and accurate recommendations. For example, a well-known recommendation algorithm, such as matrix factorization (MF), tries to optimize only the accuracy criterion, while disregarding the novelty of recommended items. In order to counteract the filter bubble, we propose two models, denoted as popularity-based and distance-based NMF, that allow to trade-off the MF performance with respect to the criteria of novelty, while only minimally compromising on accuracy. Our experimental results demonstrate that we attain high accuracy by recommending also novel items. [ABSTRACT FROM AUTHOR]

In this paper, we propose a framework based on Hierarchical Reinforcement Learning for dialogue management in a Conversational Recommender System scenario. The framework splits the dialogue into more manageable tasks whose achievement corresponds to goals of the dialogue with the user. The framework consists of a meta-controller, which receives the user utterance and understands which goal should pursue, and a controller, which exploits a goal-specific representation to generate an answer composed by a sequence of tokens. The modules are trained using a two-stage strategy based on a preliminary Supervised Learning stage and a successive Reinforcement Learning stage. [ABSTRACT FROM AUTHOR]

This paper provides an overview of our research with Pietro Torasso, outlining the main topics we worked at collaborating with him. Piero, as we are used to call him, had an extremely important role as an advisor, helping us to work in a methodologically sound way, with his constant and helpful feedback, suggestions, and forward-looking ideas. Describing our collaboration with Piero in a few pages is a difficult task, but we hope that this paper conveys at least a partial view of it. [ABSTRACT FROM AUTHOR]

Wealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients, in order to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the task, which largely requires a deep knowledge of the financial domain, a recend trend in the area is to exploit recommendation technologies to support financial advisors and to improve the effectiveness of the process. This paper proposes a framework to support financial advisors in the task of providing clients with personalized investment strategies. Our methodology is based on the exploitation of case-based reasoning. A prototype version of the platform has been adopted to generate personalized portfolios, and the performance of the framework shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings. [ABSTRACT FROM AUTHOR]

Novel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings and produce more relevant recommendations. But, when deploying a context-aware system one must put in place techniques for operating in the cold-start phase, i.e., when no or few ratings are available for the items listed in the system catalogue and it is therefore hard to predict the missing ratings and compose relevant recommendations. This problem has not been directly tackled in previous research. Hence, in order to address it, we have designed and implemented several novel algorithmic components and interface elements in a fully operational points of interest (POI) mobile recommender system (STS). In particular, in this article we illustrate the benefits brought by using the user personality and active learning techniques. We have developed two extended versions of the matrix factorisation algorithm to identify what items the users could and should rate and to compose personalised recommendations. While context-aware recommender systems have been mostly evaluated offline, a testing scenario that suffers from many limitations, in our analysis we evaluate the proposed system in live user studies where the graphical user interface and the full interaction design play a major role. We have measured the system effectiveness in terms of several metrics such as: the quality and quantity of acquired ratings-in-context, the recommendation accuracy (MAE), the system precision, the perceived recommendation quality, the user choice satisfaction, and the system usability. The obtained results confirm that the proposed techniques can effectively overcome the identified cold-start problem. [ABSTRACT FROM AUTHOR]

The Artificial Intelligence & Cultural Heritage (AI & CH) working group was born in 1999 with the aim at promoting various scientific activities to increase a more active collaboration between the sectors of cultural assets and artificial intelligence. The many events (workshops and schools) organized over the years have shown the validity of this group for exchanging ideas and gathering researchers and practitioners from different fields. New applications of informatics and artificial intelligence have provided the opportunity to produce innovative tools for documenting, managing and communicating cultural heritage. For this anniversary we intend to show how some of the most important methods and techniques of artificial intelligence developed in this area, still represent significant tools for preservation, archiving and fruition of cultural heritage. In the following the contributions of Italian researchers involved for several years in projects related to cultural heritage, will be presented. [ABSTRACT FROM AUTHOR]

Artificial Intelligence technologies are growingly used within several software systems ranging from Web services to mobile applications. It is by no doubt true that the more AI algorithms and methods are used the more they tend to depart from a pure 'AI' spirit and end to refer to the sphere of standard software. In a sense, AI seems strongly connected with ideas, methods and tools that are not (yet) used by the general public. On the contrary, a more realistic view of it would be a rich and pervading set of successful paradigms and approaches. Industry is currently perceiving semantic technologies as a key contribution of AI to innovation. In this paper a survey of current industrial experiences is used to discuss different semantic technologies at work in heterogeneous areas, ranging from Web services to semantic search and recommender systems. The resulting picture confirms the vitality of the area and allows to sketch a general taxonomy of approaches, that is the main contribution of this paper. [ABSTRACT FROM AUTHOR]