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Recommender Systems for Sustainability and Social Good: First International Workshop, RecSoGood 2024, Bari, Italy, October 18, 2024, Proceedings [Mīkstie vāki]

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  • Formāts: Paperback / softback, 162 pages, height x width: 235x155 mm, 32 Illustrations, color; 3 Illustrations, black and white; X, 162 p. 35 illus., 32 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 2470
  • Izdošanas datums: 09-Apr-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031876539
  • ISBN-13: 9783031876530
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Recommender Systems for Sustainability and Social Good: First International Workshop, RecSoGood 2024, Bari, Italy, October 18, 2024, ProceedingsPalielināt
  • Formāts: Paperback / softback, 162 pages, height x width: 235x155 mm, 32 Illustrations, color; 3 Illustrations, black and white; X, 162 p. 35 illus., 32 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 2470
  • Izdošanas datums: 09-Apr-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031876539
  • ISBN-13: 9783031876530
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031876530.html
Keywords:

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.

.- 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.