Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the risk vehicles and drivers are subject to, improving their service and reducing overhead costs. Some solutions have been proposed in the past literature for automated accident detection that are mainly based on traffic data or external sensors. However, traffic data can be difficult to access, while external sensors can end up being difficult to set up and unreliable, depending on how they are used. Additionally, the scarcity of accident detection data has limited the type of approaches used in the past, leaving in particular, machine learning (ML) relatively unexplored. Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors. Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars. In total, five different feature extraction approaches, including techniques based on feature engineering and feature learning with deep learning are evaluated on the strategic highway research program (SHRP2) naturalistic driving study (NDS) crash data set. The main observations of this study are as follows: (1) CNN features with a SVM classifier obtain very promising results, outperforming all other tested approaches. (2) Feature engineering and feature learning approaches were finding different best performing features. Therefore, our fusion experiment indicates that these two feature sets can be efficiently combined. (3) Unsupervised feature extraction remarkably achieves a notable performance score.

As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address their environmental impact. In this study, we estimate the environmental impact of recommender systems research by reproducing typical experimental pipelines. Our analysis spans 79 full papers from the 2013 and 2023 ACM RecSys conferences, comparing traditional “good old-fashioned AI’’ algorithms with modern deep learning algorithms. We designed and reproduced representative experimental pipelines for both years, measuring energy consumption with a hardware energy meter and converting it to CO2 equivalents. Our results show that papers using deep learning algorithms emit approximately 42 times more CO2 equivalents than papers using traditional methods. On average, a single deep learning-based paper generates 3,297 kilograms of CO2 equivalents—more than the carbon emissions of one person flying from New York City to Melbourne or the amount of CO2 one tree sequesters over 300 years.

When designing recommender-systems experiments, a key question that has been largely overlooked is the choice of datasets. In a brief survey of ACM RecSys papers, we found that authors typically justified their dataset choices by labelling them as public, benchmark, or ‘real-world’ without further explanation. We propose the Algorithm Performance Space (APS) as a novel method for informed dataset selection. The APS is an n-dimensional space where each dimension represents the performance of a different algorithm. Each dataset is depicted as an n-dimensional vector, with greater distances indicating higher diversity. In our experiment, we ran 29 algorithms on 95 datasets to construct an actual APS. Our findings show that many datasets, including most Amazon datasets, are clustered closely in the APS, i.e. they are not diverse. However, other datasets, such as MovieLens and Docear, are more dispersed. The APS also enables the grouping of datasets based on the solvability of the underlying problem. Datasets in the top right corner of the APS are considered ’solved problems’ because all algorithms perform well on them. Conversely, datasets in the bottom left corner lack well-performing algorithms, making them ideal candidates for new recommender-system research due to the challenges they present.

As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems community hardly understands, addresses, and evaluates the environmental impact of their work. In this study, we examine the environmental impact of recommender systems research by reproducing typical experimental pipelines. Based on our results, we provide guidelines for researchers and practitioners on how to minimize the environmental footprint of their work and implement green recommender systems — recommender systems designed to minimize their energy consumption and carbon footprint. Our analysis covers 79 papers from the 2013 and 2023 ACM RecSys conferences, comparing traditional “good old-fashioned AI” models with modern deep learning models. We designed and reproduced representative experimental pipelines for both years, measuring energy consumption using a hardware energy meter and converting it into CO ₂ equivalents. Our results show that papers utilizing deep learning models emit approximately 42 times more CO ₂ equivalents than papers using traditional models. On average, a single deep learning-based paper generates 2,909 kilograms of CO ₂ equivalents — more than the carbon emissions of a person flying from New York City to Melbourne or the amount of CO ₂ sequestered by one tree over 260 years. This work underscores the urgent need for the recommender systems and wider machine learning communities to adopt green AI principles, balancing algorithmic advancements and environmental responsibility to build a sustainable future with AI-powered personalization.

Recommender systems are pivotal in numerous industries, from e-commerce to streaming platforms, significantly influencing user choices and engagement. However, the computational costs of these systems have escalated sharply, contributing to growing environmental concerns. This paper advocates for the development of Green Recommender Systems—recommender systems designed to minimize environmental impact throughout their life cycle, from research and design to deployment and operation. While green systems strive to maintain the performance of traditional systems, they may accept trade-offs in metrics like accuracy to prioritize sustainability, often focusing on reducing energy consumption and CO2 emissions. The increasing complexity of machine learning models directly contradicts sustainability goals, and as such, a shift towards resource-efficient models is critical. We explore practical strategies to reduce the environmental footprint of recommender systems, such as using energy-efficient hardware and downsampling datasets. By incorporating sustainability into the design and evaluation of these systems, we aim to foster the development of algorithms that are both effective and environmentally responsible. This paper calls for the recommender systems community to expand the concept of performance to include energy efficiency, balancing accuracy with sustainability for the long-term viability of recommendation technologies.