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Recommender Systems for Sustainability and Social Good First International Workshop, RecSoGood 2024, Bari, Italy, October 18, 2024, Proceedings /

This CCIS post conference volume constitutes the proceedings of the First International Workshop on Recommender Systems for Sustainability and Social Good, RecSoGood 2024, in Bari, Italy, in October 2024. The 8 full papers and 6 short papers included in this book were carefully reviewed and selected...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Boratto, Ludovico (Editor), De Filippo, Allegra (Editor), Lex, Elisabeth (Editor), Ricci, Francesco (Editor)
Format: Electronic eBook
Language:English
Published: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edition:1st ed. 2025.
Series:Communications in Computer and Information Science, 2470
Subjects:
Data protection > Law and legislation.
Artificial intelligence.
Privacy.
Artificial Intelligence.
Online Access:https://doi.org/10.1007/978-3-031-87654-7
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Table of Contents:
  • -- Sustainable Development Goals; Energy and Carbon Efficiency; and conceptualizations of diversity..
  • Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs.
  • Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation.
  • Simulating the Impact of Recommendation Salience on Tourists Experienced Utility.
  • Knowledge Data Modeling in Food Recommendation: A Case Study on Nutritional Values.
  • Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach.
  • Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance.
  • EMERS: Energy Meter for Recommender Systems.
  • e-Fold Cross-Validation for Recommender-System Evaluation.
  • RecSys CarbonAtor: Predicting Carbon Footprint of Recommendation System Models.
  • Eco-Aware Graph Neural Networks for Sustainable Recommendations.
  • 14 Kg of CO2: Analyzing the Carbon Footprint and Performance of Session-Based Recommendation Algorithms.
  • From Explanation to Exploration: promoting DivErsity in Recommendation Systems.
  • Effects of Representation Nudges on the Perception of Playlist Recommendations.

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