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Recommender Systems for Sustainability and Social Good : First International Workshop, RecSoGood 2024, Bari, Italy, October 18, 2024, Proceedings / edited by Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, Francesco Ricci.
Springer Nature - Springer Computer Science (R0) eBooks 2025 English International Available online
View online- Format:
- Book
- Series:
- Communications in Computer and Information Science, 1865-0937 ; 2470
- Language:
- English
- Subjects (All):
- Data protection--Law and legislation.
- Data protection.
- Artificial intelligence.
- Privacy.
- Artificial Intelligence.
- Local Subjects:
- Privacy.
- Artificial Intelligence.
- Physical Description:
- 1 online resource (X, 162 p. 35 illus., 32 illus. in color.)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- 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.
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-87654-7
- OCLC:
- 1524424614
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