Recommender Systems

Workshop Papers

Venue Title
NeurIPS 2024 WildfireGPT: Tailored Large Language Model for Wildfire Analysis (Papers Track)
Abstract and authors: (click to expand)

Abstract: Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-specific information, particularly in areas requiring specialized knowledge, such as wildfire details within the broader context of climate change. For decision-makers focused on wildfire resilience and adaptation, it is crucial to obtain responses that are not only precise but also domain-specific. To that end, we developed WildfireGPT, a prototype LLM agent designed to transform user queries into actionable insights on wildfire risks. We enrich WildfireGPT by providing additional context, such as climate projections and scientific literature, to ensure its information is current, relevant, and scientifically accurate. This enables WildfireGPT to be an effective tool for delivering detailed, user-specific insights on wildfire risks to support a diverse set of end users, including but not limited to researchers and engineers, for making positive impact and decision making.

Authors: Yangxinyu Xie (University of Pennsylvania); Bowen Jiang (University of Pennsylvania); Tanwi Mallick (Argonne National Laboratory); Joshua Bergerson (Argonne National Laboratory); John Hutchison (Argonne National Laboratory); Duane Verner (Argonne National Laboratory); Jordan Branham (Argonne National Laboratory); M. Ross Alexander (Argonne National Laboratory); Robert Ross (Argonne National Laboratory); Yan Feng (Argonne National Laboratory); Leslie-Anne Levy (Argonne National Laboratory); Weijie Su (University of Pennsylvania); Camillo Jose Taylor (University of Pennsylvania)

NeurIPS 2024 Parakeet: Emission Factor Recommendation for Carbon Footprinting with Generative AI (Papers Track)
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Abstract: Accurately quantifying greenhouse gas (GHG) emissions from products and business activities is crucial for organizations to measure their environmental impact and undertake mitigation actions. Life cycle assessment (LCA) is the scientific discipline for measuring GHG emissions associated with each stage of a product or activity, from raw material extraction to disposal. Measuring the emissions outside of a product owner's control is challenging, and practitioners rely on emission factors (EFs) – estimates of GHG emissions per unit of activity – to model and estimate indirect impacts. These EFs come from prior LCA studies and are collated into databases. The current practice of manually finding the appropriate EF to use from databases is time-consuming, error-prone, and requires domain expertise, hindering scalability and accuracy in emissions quantification. We present a novel AI-assisted method that leverages large language models to automatically recommend EFs. Our method parses business activity descriptions and recommends the appropriate EF with a human-interpretable justification. We benchmark our solution across multiple domains and find it achieves state-of-the-art performance in EF recommendation, with an average Precision@1 of 88.4%. By streamlining and automating the EF selection process, our AI-assisted method enables scalable and accurate quantification of GHG emissions, supporting organizations' sustainability initiatives and driving progress toward net-zero emissions targets across industries.

Authors: Bharathan Balaji (Amazon); Nina Domingo (Amazon); Abu Zaher Faridee (Amazon); Venkata Sai Gargeya Vunnava (amazon); Anran Wang (Amazon); Fahimeh Ebrahimi Meymand (Amazon); Kellen Axten (Amazon); Aravind Srinivasan (Amazon); Qingshi Tu (University of British Columbia); Harsh Gupta (Amazon); Shikha Gupta (Amazon); Soma Ramalingam (Amazon); Jeremie Hakian (Amazon); Jared Kramer (Amazon)

ICLR 2024 1. Building Sustainable Futures: Tutorial on Carbon Footprint Analysis and Mitigation Strategies Using Counter Factual Queries (Tutorials Track)
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Abstract: As the sense of urgency regarding climate change continues to mount with growing regulatory pressure across the globe, it has become increasingly crucial for enterprises and governments to align their goals with sustainability values. They face a crucial imperative to act on climate change mitigation by disclosing their GHG emissions and committing to reduction and optimization of emissions from their industrial activities including operations, infrastructure, logistics, and supply chains. The world's largest enterprises have set long-term net-zero targets but lacks an integrated view of how their key business operations and processes contribute to their sustainability journey, which makes it difficult for them to embark on a well-planned journey to achieve their sustainability goals. With the recent advancement, AI intervention becomes imperative to measure, track, and improve ESG performance to achieve sustainability goals. This tutorial aims to provide a comprehensive guide on leveraging advanced AI techniques for analysing and mitigating carbon footprints in various sectors. The tutorial covers the utilization of a generalized framework that integrates sector-specific and cross-sector enterprise data, including assets and operations, to derive actionable insights. The framework also uses additional data such as weather parameters and contextual information to facilitate a holistic approach to carbon footprint analysis and its mitigation strategies. The tutorial will delve into the working of a framework which comprises of an LLM driven carbon accounting engine, predictive models for carbon emissions, anomaly detection models, and counterfactual models. It identifies the emission hotspots, thereafter provides actionable recommendations to mitigate the carbon emission. The proposed tutorial aims to empower participants with the knowledge and skills to make informed decisions towards building a more sustainable future

Authors: Kumar Saurav (IBM); Manikandan Padmanaban (IBM Research India); Ayush Jain (IBM Research); Jagabondhu Hazra (IBM Research India)

NeurIPS 2023 Towards Recommendations for Value Sensitive Sustainable Consumption (Papers Track)
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Abstract: Excessive consumption can strain natural resources, harm the environment, and widen societal gaps. While adopting a more sustainable lifestyle means making significant changes and potentially compromising personal desires, balancing sustainability with personal values poses a complex challenge. This article delves into designing recommender systems using neural networks and genetic algorithms, aiming to assist consumers in shopping sustainably without disregarding their individual preferences. We approach the search for good recommendations as a problem involving multiple objectives, representing diverse sustainability goals and personal values. While using a synthetic historical dataset based on real-world sources, our evaluations reveal substantial environmental benefits without demanding drastic personal sacrifices, even if consumers accept only a fraction of the recommendations.

Authors: Thomas Asikis (University of Zurich)

ICLR 2023 Activity-Based Recommendations for the Reduction of CO2 Emissions in Private Households (Papers Track)
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Abstract: This paper proposes an activity prediction framework for a multi-agent recommendation system to tackle the energy-efficiency problem in residential buildings. Our system generates an activity-shifting schedule based on the social practices from the users’ domestic life. We further provide a utility option for the recommender system to focus on saving CO2 emissions or energy costs, or both. The empirical results show that while focusing on the reduction of CO2 emissions, the system provides an average of 12% of emission savings and 7% of electricity cost savings. When concentrating on energy costs, 6% of emission savings and 20% of electricity cost savings are possible for the studied households.

Authors: Alona Zharova (Humboldt University of Berlin); Laura Löschmann (Humboldt University of Berlin)

NeurIPS 2022 Explainable Multi-Agent Recommendation System for Energy-Efficient Decision Support in Smart Homes (Papers Track)
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Abstract: Transparent, understandable, and persuasive recommendations support the electricity consumers’ behavioral change to tackle the energy efficiency problem. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we extend a novel multi-agent approach by designing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models (i.e., k-nearest-neighbours, extreme gradient boosting, adaptive boosting, random forest, logistic regression, and explainable boosting machines). Since we want to help the user understand a single recommendation, we focus on local explainability approaches. In particular, we apply post-model approaches LIME (local, interpretable, model-agnostic explanation) and SHAP (Shapley additive explanations) as model-agnostic tools that can explain the predictions of the chosen classifiers. We further provide an overview of the predictive and explainability performance. Our results show a substantial improvement in the performance of the multi-agent system while at the same time opening up the “black box” of recommendations. To show the pathway to positive impact regarding climate change, we provide a discussion on the potential impact of the suggested approach.

Authors: Alona Zharova (Humboldt University of Berlin); Annika Boer (Humboldt University of Berlin); Julia Knoblauch (Humboldt University of Berlin); Kai Ingo Schewina (Humboldt University of Berlin); Jana Vihs (Humboldt University of Berlin)

NeurIPS 2021 Capturing Electricity Market Dynamics in the Optimal Trading of Strategic Agents using Neural Network Constrained Optimization (Papers Track)
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Abstract: In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporate complex market dynamics in the optimal bidding strategy. This method uses input-convex neural networks (ICNNs) to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., to replace the lower-level problem by a neural network. In a comparative analysis, the optimal bidding problem of a load agent is formulated via the proposed MPNNC and via the classical bi-level programming method, and compared against each other.

Authors: Mihály Dolányi (KU Leuven); Kenneth Bruninx (KU Leuven); Jean-François Toubeau (Faculté Polytechnique (FPMs), Université de Mons (UMONS)); Erik Delaue (KU Leuven)