In an era where personalization technologies increasingly shape human decision-making, integrating sustainability and social good into user-adaptive systems has become an essential challenge. Building on recent advances in User Modeling, Personalization, and Adaptive Systems, this workshop aims to consolidate a research agenda focused on how personalization can support sustainable behaviors, ethical decision-making, inclusion, and positive societal impact. Personalized and adaptive systems influence users’ choices across many contexts—health, mobility, education, media consumption, and more. Their role in shaping long-term behavioral change positions them as key technologies for supporting the UN Sustainable Development Goals and broader sustainability initiatives. This workshop provides an interdisciplinary venue to explore theoretical, methodological, and practical advances at the intersection of sustainability, personalization, and adaptive systems. Through presentations, discussions, and interactive sessions, the workshop aims to stimulate knowledge exchange and collaboration for developing sustainable personalized systems.

In the rapidly evolving landscape of technology and sustainability, leveraging Recommender Systems has emerged as a powerful tool for driving positive change. With a foundation in AI and data analytics, Recommender Systems can be effective in various domains, from e-commerce to energy management, inclusion and well-being. By harnessing the power of recommendation algorithms under a multi-stakeholder perspective, organizations and researchers can guide users towards more sustainable choices and behaviors, contributing to broader environmental and social goals. With this aim, our workshop provides a unique platform for researchers, practitioners, and platform owners to explore the integration of sustainability principles into Recommender Systems. Through presentations, discussions, and panels, participants can explore the theoretical foundations, practical implementations, and ethical and environmental considerations of sustainable Recommender Systems. By fostering collaboration and knowledge exchange, the workshop aims to catalyze innovation and inspire collective action towards a more sustainable future.

With the growing focus on Green AI, there is an urgent need for algorithms that are designed to minimize their environmental impact while maintaining satisfying performance.In this paper, we introduce a novel early stopping strategy that considers carbon footprint data while training a recommendation algorithm.In particular, during the training phase, our criterion epoch-by-epoch analyzes the improvement in terms of predictive accuracy and compares it to the increase in carbon emissions.Then, we analyze the trade-off between the scores, and when the accuracy improves at a rate that is not favorable, the training is stopped.In the experimental evaluation, we showed that our strategy could significantly reduce the carbon footprint of several state-ofthe-art recommendation models, with a limited decrease in accuracy and fairness.While more work is needed to automatically balance the trade-off between accuracy and emissions, this paper sheds light on the need for more sustainable recommendation models and takes a significant step toward designing green training strategies.

Abstract Predicting the execution time of weather forecast models is a complex task, since these models are usually performed on High Performance Computing systems that require large computing capabilities. Indeed, a reliable prediction can imply several benefits, by allowing for an improved planning of the model execution, a better allocation of available resources, and the identification of possible anomalies. However, to make such predictions is usually hard, since there is a scarcity of datasets that benchmark the existing meteorological simulation models. In this work, we focus on the runtime predictions of the execution of the COSMO (COnsortium for SMall-scale MOdeling) weather forecasting model used at the Hydro-Meteo-Climate Structure of the Regional Agency for the Environment and Energy Prevention Emilia-Romagna. We show how a plethora of Machine Learning approaches can obtain accurate runtime predictions of this complex model, by designing a new well-defined benchmark for this application task. Indeed, our contribution is twofold: 1) the creation of a large public dataset reporting the runtime of COSMO run under a variety of different configurations; 2) a comparative study of ML models, which greatly outperform the current state-of-practice used by the domain experts. This data collection represents an essential initial benchmark for this application field, and a useful resource for analyzing the model performance: better accuracy in runtime predictions could help facility owners to improve job scheduling and resource allocation of the entire system; while for a final user, a posteriori analysis could help to identify anomalous runs.

In the rapidly evolving landscape of technology and sustainability, leveraging Recommender Systems has emerged as a powerful tool for driving positive change. With a foundation in AI and data analytics, Recommender Systems can be effective in various domains, from e-commerce to energy management and well-being. By harnessing the power of recommendation algorithms under a holistic perspective, organizations and researchers can guide users towards more sustainable choices and behaviors, contributing to broader environmental and social goals. With this aim, our workshop provides a unique opportunity for researchers, practitioners, and stakeholders to explore the integration of sustainability principles into Recommender Systems. Through presentations, discussions, and panels, participants explore the theoretical foundations, practical implementations, and ethical and environmental issues of sustainable Recommender Systems. By fostering collaboration and knowledge exchange, the workshop aims to catalyze innovation and inspire collective action towards a more sustainable future.

This work investigates the path toward green recommender systems by examining the impact of data reduction on both model performance and carbon footprint. In the pursuit of developing energy-efficient recommender systems, we investigated whether and how reducing the training data impacts the performances of several representative recommendation models. In order to obtain a fair comparison, all the models were run based on the implementations available in a popular recommendation library, i.e., RecBole, and used the same experimental settings. Results indicate that: (a) data reduction can be a promising strategy to make recommender systems more sustainable, at the cost of a lower accuracy; (b) training recommender systems with less data makes the suggestions more diverse and less biased. Overall, this study contributes to the ongoing discourse on the development of recommendation models that meet the principles of SDGs, laying the groundwork for the adoption of more sustainable practices in the field.

The integration of Machine Learning (ML) and Constrained Optimization (CO) techniques has recently gained significant interest. While pure CO methods struggle with scalability and robustness, and ML methods like constrained Reinforcement Learning (RL) face difficulties with combinatorial decision spaces and hard constraints, a hybrid approach shows promise. However, multi-stage decision-making under uncertainty remains challenging for current methods, which often rely on restrictive assumptions or specialized algorithms. This paper introduces unify , a versatile framework for tackling a wide range of problems, including multi-stage decision-making under uncertainty, using standard ML and CO components. unify integrates a CO problem with an unconstrained ML model through parameters controlled by the ML model, guiding the decision process. This ensures feasible decisions, minimal costs over time, and robustness to uncertainty. In the empirical evaluation, unify demonstrates its capability to address problems typically handled by Decision Focused Learning, Constrained RL, and Stochastic Optimization. While not always outperforming specialized methods, unify ’s flexibility offers broader applicability and maintainability . The paper includes the method’s formalization and empirical evaluation through case studies in energy management and production scheduling, concluding with future research directions.

Recent developments in the field of deep learning have steered research on music generation systems towards a massive use of large end-to-end neural architectures. The capability of these systems to produce convincing outputs has been extensively proven. Nonetheless, they usually come with several drawbacks, such as a low degree of user control, a lack of global structure, and the inherent impossibility of online generation due to high computational costs. Our contribution is two-fold: first, we identify these limitations and show how they have been discussed and partially addressed in the existing literature; then, we propose a novel music generation approach aimed at overcoming such limitations, by properly combining a set of samples under user-defined constraints. We model our task as a job-shop problem, and we show that interesting results can be obtained at very low computational costs. Our framework is genre-independent as it deals with samples metadata rather then individual notes, even though additional genre-specific constraint could be introduced by users to meet their stylistic requirements.

Artificial Intelligence (AI) has gradually attracted attention in the field of artistic creation, resulting in a debate on the evaluation of AI artistic outputs. However, there is a lack of common criteria for objective artistic evaluation both of human and AI creations. This is a frequent issue in the field of dance, where different performance metrics focus either on evaluating human or computational skills separately. This work proposes a methodological approach for the artistic evaluation of both AI and human artistic creations in the field of robotic dance. First, we define a series of common initial constraints to create robotic dance choreographies in a balanced initial setting, in collaboration with a group of human dancers and choreographer. Then, we compare both creation processes through a human audience evaluation. Finally, we investigate which choreography aspects (e.g., the music genre) have the largest impact on the evaluation, and we provide useful guidelines and future research directions for the analysis of interconnections between AI and human dance creation.

In this paper, we present a comparative analysis of the trade-off between the performance of state-of-the-art recommendation algorithms and their environmental impact. In particular, we compared 18 popular recommendation algorithms in terms of both performance metrics (i.e., accuracy and diversity of the recommendations) as well as in terms of energy consumption and carbon footprint on three different datasets. In order to obtain a fair comparison, all the algorithms were run based on the implementations available in a popular recommendation library, i.e., RecBole, and used the same experimental settings. The outcomes of the experiments showed that the choice of the optimal recommendation algorithm requires a thorough analysis, since more sophisticated algorithms often led to tiny improvements at the cost of an exponential increase of carbon emissions. Through this paper, we aim to shed light on the problem of carbon footprint and energy consumption of recommender systems, and we make the first step towards the development of sustainability-aware recommendation algorithms.

This paper considers multi-stage optimization problems under uncertainty that involve distinct offline and online phases. In particular it addresses the issue of integrating these phases to show how the two are often interrelated in real-world applications. Our methods are applicable under two (fairly general) conditions: 1) the uncertainty is exogenous; 2) it is possible to define a greedy heuristic for the online phase that can be modeled as a parametric convex optimization problem. We start with a baseline composed by a two-stage offline approach paired with the online greedy heuristic. We then propose multiple methods to tighten the offline/online integration, leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. Overall, our methods provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios. To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits.

In this work we consider optimization problems that require to make interdependent offline and online decisions under uncertainty.We broadly refer to long-term strategic decisions as offline and to short-term operational decisions as online.For example, in Distributed Energy Management Systems we may need to define (offline) a daily production schedule for an industrial plant, and then manage (online) its power supply on a hour by hour basis.Traditionally offline and online phases are tackled in isolation, leading to some drawbacks: offline decisions are taken without regard for the capabilities of the downstream online solver; while the applicability of the best approaches for online decisions (e.g.anticipatory algorithms) is limited by the need to provide high responsiveness.Starting from a (literature-based) baseline, we define general methods for leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase.All our methods have broad applicability, and provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios with both offline and online decisions and featuring continuous and discrete decisions.An extensive analysis of the experimental results shows that offline/online integration may lead to substantial benefits.

Optimization problems under uncertainty are traditionally solved either via offline or online methods. Offline approaches can obtain high-quality robust solutions, but have a considerable computational cost. Online algorithms can react to unexpected events once they are observed, but often run under strict time constraints, preventing the computation of optimal solutions. Many real world problems, however, have both offline and online elements: a substantial amount of time and information is frequently available (offline) before an online problem is solved (e.g. energy production forecasts, or historical travel times in routing problems); in other cases both offline (i.e. strategic) and online (i.e. operational) decisions need to be made. Surprisingly, the interplay of these offline and online phases has received little attention: like in the blind men and the elephant tale, we risk missing the whole picture, and the benefits that could come from integrated offline/online optimization. In this survey we highlight the potential shortcomings of pure methods when applied to mixed offline/online problems, we review the strategies that have been designed to take advantage of this integration, and we suggest directions for future research.

Sampling-based anticipatory algorithms can be very effective at solving online optimization problems under uncertainty, but their computational cost may be prohibitive in some cases. Given an arbitrary anticipatory algorithm, we present three methods that allow to retain its solution quality at a fraction of the online computational cost, via a substantial degree of offline preparation. Our approaches are obtained by combining: 1) a simple technique to identify likely future outcomes based on past observations; 2) the (expensive) offline computation of a "contingency table"; and 3) an efficient solution-fixing heuristic. We ground our techniques on two case studies: an energy management system with uncertain renewable generation and load demand, and a traveling salesman problem with uncertain travel times. In both cases, our techniques achieve high solution quality, while substantially reducing the online computation time.

In this work we present two general techniques to deal with multi-stage optimization problems under uncertainty, featuring off-line and on-line decisions. The methods are applicable when: 1) the uncertainty is exogenous; 2) there exists a heuristic for the on-line phase that can be modeled as a parametric convex optimization problem. The first technique replaces the on-line heuristics with an anticipatory solver, obtained through a systematic procedure. The second technique consists in making the off-line solver aware of the on-line heuristic, and capable of controlling its parameters so as to steer its behavior. We instantiate our approaches on two case studies: an energy management system with uncertain renewable generation and load demand, and a vehicle routing problem with uncertain travel times. We show how both techniques achieve high solution quality w.r.t. an oracle operating under perfect information, by obtaining different trade-offs in terms of computation time.