Recommender Systems (RS) influence everyday decisions, yet most remain optimized for short-term engagement or commercial gain. RS4SD aims to shift this focus by exploring how RS can contribute to sustainable development through behavioral change and nudging strategies. Aligned with the UN Sustainable Development Goals (SDG), RS4SD will highlight applications that promote responsible consumption, sustainable mobility, healthy eating, and digital well-being. In particular, we will focus on how AI and RS can be designed to foster sustainable behaviors through multi-objective optimization and ethically aligned interventions. These objectives are directly tied to the UN SDG, and we welcome all contributions showcasing RS in support of these goals. A central theme of the workshop is the integration of behavioral science and AI to design interventions that guide users toward more sustainable and healthier choices while preserving individual autonomy. Topics of interest include multi-objective recommendation, health-aware RS, eco-friendly product and tourism RS, as well as novel evaluation metrics that go beyond accuracy to capture societal impact. RS4SD will bring together researchers, stakeholders and practitioners from RS, AI, sustainability, and behavioral science to share models, datasets, frameworks, and real-world use cases. The workshop encourages interdisciplinary collaboration and aims to build a community dedicated to responsible, behavior-aware RS that benefit both individuals and society.

Abstract Food recommendation systems have become increasingly popular due to the proliferation of online food service websites. Accordingly, the ratings assigned by users are one of the most important resources in these systems. However, users generally express their opinions about a few foods, which results in data sparsity. Furthermore, food recommendation is a health-critical task, as recommending unhealthy foods to users may threaten their health. In this paper, we developed a novel rating profile expansion approach for food recommenders that considers both health and reliability measures. This approach enhances the efficiency of the user’s rating profile by including healthy and reliable virtual ratings. Specifically, we introduce a probabilistic rating profile evaluation technique to determine whether a profile needs to be expanded. Then, those profiles with an insufficient number of ratings are automatically expanded by adding virtual ratings obtained using the opinions of users who belong to the target user’s community. For this purpose, the users are grouped using a novel time-aware community detection algorithm based on their preferences. Moreover, a health-aware reliability measure is proposed so that only the most reliable virtual ratings are accounted for in the target user’s rating profile expansion. Therefore, the developed approach not only mitigates issues stemming from sparse data in food recommendation systems but also makes them more effective in recommending healthy foods to users. Experiments conducted on two publicly available real-world datasets demonstrated that the developed system is superior to other baseline models.

This paper reports on the DISH4U, a mobile app designed to guide the users towards healthy foods with a special focus on Finnish market. The application makes use of the Finnish Institute of Health and Welfare (THL) nutritional database Fineli to determine the nutritional content of the food. Besides, the app takes into account user’s profile in terms of food preferences, health and sport activities, and then elucidating the user about nutritional content of food served at selected restaurants as well as providing healthy recommendations available at nearby restaurants.

In recent years, the surge in social media platform usage has sparked a heightened interest in applying recommender systems (RSs) within the food industry. Traditionally, the exploration of user interests focused on analyzing behaviors linked to food selection. The availability of user interaction datasets now provides avenues for deeper insights into food content and intricate user relationships. This paper advocates strategically integrating Heterogeneous Information Networks (HIN) into recommender system frameworks. It introduces the Heterogeneous Attention Network-based User Interests-Aware Food Recommender System (UIFRS-HAN), designed for personalized food recommendations. By leveraging HIN and a two-step attention mechanism, UIFRS-HAN captures diverse entities and relationships within a unified framework. UIFRS-HAN employs an attention technique to reconstruct node features and edges, incorporating a dual hierarchical attention mechanism for improved unsupervised learning of attributed graph representations. Besides, HIN allows the model to uncover meaningful relationships between nodes, particularly when directed relationships are unclear. Through a defined meta-path-based attention mechanism, UIFRS-HAN generates diverse recommendations based on users’ interests across various relations among different types of nodes of the HIN. By discerning intricate patterns and correlations, UIFRS-HAN surpasses traditional approaches in delivering refined and contextually relevant recommendations. The proposed model enhances representation depth and accuracy by employing node embedding through a hierarchical meta-path structure. Rigorous testing on Allrecipes.com and Food.com datasets, compared against 15 baselines and state-of-the-art models, confirms the technical soundness and superiority of UIFRS-HAN in providing precise and personalized food recommendations. • A novel food recommender system based on a heterogeneous attention network. • The dual attention method is used to learn the meta-path in HIN. • Employing unsupervised learning based on hierarchical attention in the HIN. • The experiment method involves two real datasets based on heterogeneous graphs.

Existing food recommendation models have typically suggested foods or recipes to single users. However, in reality, users may be members of a group, family, or community, requiring food recommendation systems to support the whole group. Food recommendations to groups are a more challenging task than food recommendations to individuals, as each person’s preferences in the group should be addressed before giving the recommendations. Suggesting healthy food is also important in a food recommendation system, given that unhealthy diets can lead to different diseases. To address these challenges, a new healthy group food recommendation system based on deep social community detection and user popularity is developed in this study. To this end, an innovative deep community detection approach based on feature learning and deep neural networks is developed using the calculated time-aware user similarity measure. In addition, a health-aware rate prediction measurement, which considers both group preferences and health factors, is developed. Different experiments are designed on two real-food social networks to specify the efficiency of the suggested model, and the results indicate that it enhanced the single-user and group satisfaction metrics.

Food recommendation systems play a crucial role in promoting personalized recommendations designed to help users find food and recipes that align with their preferences. However, many existing food recommendation systems have overlooked the important aspect of healthy-food and nutritional value of recommended foods, thereby limiting their effectiveness in generating truly healthy recommendations. Our preliminary analysis indicates that users tend to respond positively to unhealthy food and recipes. As a result, existing food recommender systems that neglect health considerations often assign high scores to popular items, inadvertently encouraging unhealthy choices among users. In this study, we propose the development of a fairness-based model that prioritizes health considerations. Our model incorporates fairness constraints from both the user and item perspectives, integrating them into a joint objective framework. Experimental results conducted on real-world food datasets demonstrate that the proposed system not only maintains the ability of food recommendation systems to suggest users’ favorite foods but also improves the health factor compared to unfair models, with an average enhancement of approximately 35%.

Food recommendation systems aim to provide recommendations according to a user’s diet, recipes, and preferences. These systems are deemed useful for assisting users in changing their eating habits towards a healthy diet that aligns with their preferences. Most previous food recommendation systems do not consider the health and nutrition of foods, which restricts their ability to generate healthy recommendations. This paper develops a novel health-aware food recommendation system that explicitly accounts for food ingredients, food categories, and the factor of time, predicting the user’s preference through time-aware collaborative filtering and a food ingredient content-based model. Based on the user's predicted preferences and the health factor of each food, our model provides final recommendations to the target user. The performance of our model was compared to several state-of-the-art recommender systems in terms of five distinct metrics: Precision, Recall, F1, AUC, and NDCG. Experimental analysis of datasets extracted from the websites Allrecipes.com and Food.com demonstrated that our proposed food recommender system performs well compared to previous food recommendation models.

The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-the-art studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods.

In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity.

Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task.

With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches.