{"product_id":"9783031876530","title":"Recommender Systems for Sustainability and Social Good : First International Workshop, RecSoGood 2024, Bari, Italy, October 18, 2024, Proceedings (Communications in Computer and Information Science 2470) (2025. x, 162 S. X, 162 p. 35 illus., 32 illus. in","description":"\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003e.- Sustainable Development Goals; Energy and Carbon Efficiency; and conceptualizations of diversity..\u003c\/p\u003e\u003cp\u003e.- Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs.\u003c\/p\u003e\u003cp\u003e.- Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation.\u003c\/p\u003e\u003cp\u003e.- Simulating the Impact of Recommendation Salience on Tourists Experienced Utility.\u003c\/p\u003e\u003cp\u003e.- Knowledge Data Modeling in Food Recommendation: A Case Study on Nutritional Values.\u003c\/p\u003e\u003cp\u003e.- Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach.\u003c\/p\u003e\u003cp\u003e.- Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance.\u003c\/p\u003e\u003cp\u003e.- EMERS: Energy Meter for Recommender Systems.\u003c\/p\u003e\u003cp\u003e.- e-Fold Cross-Validation for Recommender-System Evaluation.\u003c\/p\u003e\u003cp\u003e.- RecSys CarbonAtor: Predicting Carbon Footprint of Recommendation System Models.\u003c\/p\u003e\u003cp\u003e.- Eco-Aware Graph Neural Networks for Sustainable Recommendations.\u003c\/p\u003e\u003cp\u003e.- 14 Kg of CO2: Analyzing the Carbon Footprint and Performance of Session-Based Recommendation Algorithms.\u003c\/p\u003e\u003cp\u003e.- From Explanation to Exploration: promoting DivErsity in Recommendation Systems.\u003c\/p\u003e\u003cp\u003e.- Effects of Representation Nudges on the Perception of Playlist Recommendations.\u003c\/p\u003e","brand":"SPRINGER, BERLIN; SPRINGER NATURE SWITZERLAND; SPRING","offers":[{"title":"Default Title","offer_id":48865598505195,"sku":"00000_00000_00000_00000","price":164.78,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9783031876530-1.jpg?v=1781717878","url":"https:\/\/kinokuniya.com.sg\/products\/9783031876530","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}