Recommender Systems for Sustainability and Social Good

First International Workshop, RecSoGood 2024, Bari, Italy, October 18, 2024, Proceedings
Erscheinungsjahr 2025
ISBN: 978-3-031-87653-0
Verlag: Springer Nature Switzerland

Buch, Englisch, 162 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 271 g

Reihe: 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.

Zielgruppe

Research

Autoren/Hrsg.

Boratto, Ludovico

Ricci, Francesco

De Filippo, Allegra

Lex, Elisabeth

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz

Inhaltsverzeichnis

.- 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.