Recommender Systems for Sustainability and Social Good: First International Workshop, RecSoGood 2024,...

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Ludovico Boratto Allegra De Filippo

Recommender Systems for Sustainability and Social Good

First International Workshop, RecSoGood 2024, Bari, Italy, October 18, 2024, Proceedings

Ludovico Boratto, Allegra De Filippo, Elisabeth Lex , Francesco Ricci

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English

Springer International Publishing AG
09 April 2025

Privacy & data protection; Artificial intelligence

Series: Communications in Computer and Information Science

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 from 35 submissions. They cover all aspects of Recommender Systems for Sustainable Development Goals; Energy and Carbon Efficiency; and conceptualizations of diversity.

Edited by: Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, Francesco Ricci
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Volume: 2470
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783031876530
ISBN 10: 3031876539
Series: Communications in Computer and Information Science
Pages: 162
Publication Date: 09 April 2025
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Active

.- 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.