Giuseppe Sansonetti | Roma Tre University, Rome, Italy (original) (raw)

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Papers by Giuseppe Sansonetti

Springer eBooks, Aug 14, 2007

International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015

This article reports our experience in developing a recommender system (RS) able to suggest relev... more This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies on a user profile represented as a set of weighted concepts related to the user's interests. The weighting function, we named sentiment-volume-objectivity (SVO) function, takes into account not only the user's sentiment toward his/her interests, but also the volume and objectivity of related contents. A clustering technique based on modularity optimization enables us to identify the latent sentiment communities. A preliminary experimental evaluation on real-world datasets from Twitter shows the benefits of the proposed approach and allows us to make some considerations about the detected communities.

Lecture Notes in Computer Science, 2023

This is the third edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SO... more This is the third edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) workshop. As in the two previous editions, also this year, the main objective is to create an opportunity for all those interested in exploring and implementing new technologies to promote the inclusion of people by breaking down possible cultural, social, and language obstacles. Particular attention is paid to those who may have more difficulty than others in establishing interpersonal connections. To achieve this ambitious goal, social robots can also play a key role. This year, the invited talk will focus on models and methods to make human-robot interaction more and more effective. The final goal is to provide the robot with the perspective capabilities typical of human beings and the ability to adapt and learn. Only in this way, the robot will be able to establish an effective connection with any human being, overcoming the difficulties arising from the differences between the traits that characterize one individual compared to another. CCS CONCEPTS • Human-centered computing → User interface design; User centered design; Accessibility technologies; User studies; • Information systems → Recommender systems; • Social and professional topics → People with disabilities; Assistive technologies; Cultural characteristics.

arXiv (Cornell University), Sep 8, 2022

Finding online research papers relevant to one's interests is very challenging due to the increas... more Finding online research papers relevant to one's interests is very challenging due to the increasing number of publications. Therefore, personalized research paper recommendation has become a significant and timely research topic. Collaborative filtering is a successful recommendation approach, which exploits the ratings given to items by users as a source of information for learning to make accurate recommendations. However, the ratings are often very sparse as in the research paper domain, due to the huge number of publications growing every year. Therefore, more attention has been drawn to hybrid methods that consider both ratings and content information. Nevertheless, most of the hybrid recommendation approaches that are based on text embedding have utilized bag-ofwords techniques, which ignore word order and semantic meaning. In this paper, we propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users. The experimental evaluation is performed on CiteULike, a real and publicly available dataset. The obtained findings show that the proposed model is effective in recommending research papers even when the rating data is very sparse.

Workshop on Artificial Intelligence, Vision and Pattern Recognition, 2001

Communications in computer and information science, 2021

Communications in computer and information science, 2020

Conference on Recommender Systems, 2015

Everyday video-sharing websites such as YouTube collect large amounts of new multimedia resources... more Everyday video-sharing websites such as YouTube collect large amounts of new multimedia resources. Comments left by viewers often provide valuable information to describe sentiments, opinions and tastes of users. For this reason, we propose a novel re-ranking approach that takes into consideration that information in order to provide better recommendations of related videos. Early experiments indicate an improvement in the recommendation performance.

IEEE EUROCON 2023 - 20th International Conference on Smart Technologies

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

Recommender systems (RSs) are increasingly present in our everyday lives for business and pleasur... more Recommender systems (RSs) are increasingly present in our everyday lives for business and pleasure. The Cultural Heritage domain is no exception. In the research literature, several RSs have been proposed to enhance the fruition of artistic and cultural resources. In this paper, we present some of our research activities aimed at realizing a RS for suggesting personalized itineraries to exhibit and museum visitors. More specifically, we describe the collection and use of eye-tracking data to understand if there are any correlations between the visitors' gaze patterns and their degree of appreciation of the viewed artworks. If such correlations exist, they could be used as implicit feedback in the recommendation engine. The preliminary results are interesting and encourage us to pursue our research activities. CCS CONCEPTS • Information systems → Recommender systems; • Humancentered computing → User models; • Computing methodologies → Tracking.

Communications in computer and information science, 2022

Nowadays, Machine Learning (ML) is present in a high number of application fields. Among these, t... more Nowadays, Machine Learning (ML) is present in a high number of application fields. Among these, there is also automatic trading in the financial sector. The research question underlying our research activities is as follows: can ML techniques provide added value in the prediction task in domains with high volatility such as the cryptocurrency financial market? To answer this question, we analyzed and compared different Reinforcement Learning (RL) algorithms on data publicly available online. Specifically, we tested some value-based and policy-based RL algorithms trained for different time intervals, with diverse hyperparameter values and reward functions. The agent that allowed us to achieve the best results was the Deep Recurrent Q-Network trained using the Sharpe ratio as a reward function.

Applied Sciences, 2022

Nowadays, technology makes it possible to admire objects and artworks exhibited all over the worl... more Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e.,...

Applied Sciences

Although tremendous advances have been made in recent years, many real-world problems still canno... more Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone [...]

Nowadays, several location-based services (LBSs) allow their users to take advantage of informati... more Nowadays, several location-based services (LBSs) allow their users to take advantage of information from the Web about points of interest (POIs) such as cultural events or restaurants. To the best of our knowl-edge, however, none of these provides information taking into account user preferences, or other elements, in addition to location, that contribute to define the context of use. The provided suggestions do not consider, for example, time, day of week, weather, user activity or means of transport. This paper describes a social recommender system able to identify user preferences and information needs, thus suggesting personalized recommendations related to POIs in the surroundings of the user’s current location. The proposed approach achieves the following goals: (i) to supply, unlike the current LBSs, a methodology for identifying user pref-erences and needs to be used in the information filtering process; (ii) to exploit the ever-growing amount of information from social netw...