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View article: Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization Open
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems community hardly understands, addresses, and evaluates the enviro…
View article: Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization Open
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems community hardly understands, addresses, and evaluates the enviro…
View article: On the Reliability of Sampling Strategies in Offline Recommender Evaluation
On the Reliability of Sampling Strategies in Offline Recommender Evaluation Open
Offline evaluation plays a central role in benchmarking recommender systems when online testing is impractical or risky. However, it is susceptible to two key sources of bias: exposure bias, where users only interact with items they are sh…
View article: The Hidden Cost of Defaults in Recommender System Evaluation
The Hidden Cost of Defaults in Recommender System Evaluation Open
Hyperparameter optimization is critical for improving the performance of recommender systems, yet its implementation is often treated as a neutral or secondary concern. In this work, we shift focus from model benchmarking to auditing the b…
View article: Early Explorations of Recommender Systems for Physical Activity and Well-being
Early Explorations of Recommender Systems for Physical Activity and Well-being Open
As recommender systems increasingly guide physical actions, often through wearables and coaching tools, new challenges arise around how users interpret, trust, and respond to this advice. This paper introduces a conceptual framework for ta…
View article: Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters Open
In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research …
View article: Soundtracks of Our Lives: How Age Influences Musical Preferences
Soundtracks of Our Lives: How Age Influences Musical Preferences Open
The majority of research in recommender systems, be it algorithmic improvements, context-awareness, explainability, or other areas, evaluates these systems on datasets that capture user interaction over a relatively limited time span. Howe…
View article: Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations
Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations Open
Recommender systems play a vital role in helping users discover content in streaming services, but their effectiveness depends on users understanding why items are recommended. In this study, explanations were based solely on item features…
View article: Trends in natural language processing for text classification: A comprehensive survey
Trends in natural language processing for text classification: A comprehensive survey Open
Text classification has become a cornerstone in natural language processing (NLP), facilitating a wide range of applications such as sentiment analysis, spam detection, and hate speech moderation. This comprehensive survey explores the his…
View article: On explaining recommendations with Large Language Models: a review
On explaining recommendations with Large Language Models: a review Open
The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging L…
View article: Recommender Systems for Social Good: The Role of Accountability and Sustainability
Recommender Systems for Social Good: The Role of Accountability and Sustainability Open
This work examines the role of recommender systems in promoting sustainability, social responsibility, and accountability, with a focus on alignment with the United Nations Sustainable Development Goals (SDGs). As recommender systems becom…
View article: On Explaining Recommendations with Large Language Models: A Review
On Explaining Recommendations with Large Language Models: A Review Open
The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging L…
View article: Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters Open
In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research …
View article: Understanding Fairness in Recommender Systems: A Healthcare Perspective
Understanding Fairness in Recommender Systems: A Healthcare Perspective Open
Fairness in AI-driven decision-making systems has become a critical concern,\nespecially when these systems directly affect human lives. This paper explores\nthe public's comprehension of fairness in healthcare recommendations. We\nconduct…
View article: From Clicks to Carbon: The Environmental Toll of Recommender Systems
From Clicks to Carbon: The Environmental Toll of Recommender Systems Open
As global warming soars, the need to assess the environmental impact of\nresearch is becoming increasingly urgent. Despite this, few recommender systems\nresearch papers address their environmental impact. In this study, we estimate\nthe e…
View article: EMERS: Energy Meter for Recommender Systems
EMERS: Energy Meter for Recommender Systems Open
Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems community often does not report the energy consumption of their exp…
View article: Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems
Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems Open
The adoption of recommender systems (RSs) in various domains has become increasingly popular, but concerns have been raised about their lack of transparency and interpretability. While significant advancements have been made in creating ex…
View article: Trust Through Recommendation in E-commerce
Trust Through Recommendation in E-commerce Open
We explore the influence of recommender systems on trust among consumers in the fashion e-commerce domain. Anchoring on the Trust Building Model (TBM) [13], we investigate its adaptability and applicability in the context of interactive co…
View article: Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation
Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation Open
Evaluation plays a vital role in recommender systems—in research and practice—whether for confirming algorithmic concepts or assessing the operational validity of designs and applications. It may span the evaluation of early ideas and appr…
View article: Leveraging Large Language Models for Goal-driven Interactive Recommendations
Leveraging Large Language Models for Goal-driven Interactive Recommendations Open
We present a proof of concept application for interactive recommendations and explanations leveraging the capabilities of Large Language Models (LLMs). The application creates a highly interactive user-driven setting for recommendations gi…
View article: Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives
Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives Open
Recommender systems research and practice are fast-developing topics with growing adoption in a wide variety of information access scenarios. In this article, we present an overview of research specifically focused on the evaluation of rec…
View article: Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education
Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education Open
This report documents the program and the outcomes of Dagstuhl Seminar 23031 "Frontiers of Information Access Experimentation for Research and Education", which brought together 38 participants from 12 countries. The seminar addressed tech…
View article: Reality Check – Conducting Real World Studies
Reality Check – Conducting Real World Studies Open
Information retrieval and recommender systems are deployed in real world environments. Therefore, to get a real feeling for the system, we should study their characteristics in “real world studies”. This raises the question: What does it m…
View article: Prediction Accuracy and Autonomy
Prediction Accuracy and Autonomy Open
The tech industry has been criticised for designing applications that undermine individuals' autonomy. Recommender systems, in particular, have been identified as a suspected culprit that might exercise unwanted control over peoples' lives…
View article: Report on the 1st workshop on the perspectives on the evaluation of recommender systems (PERSPECTIVES 2021) at RecSys 2021
Report on the 1st workshop on the perspectives on the evaluation of recommender systems (PERSPECTIVES 2021) at RecSys 2021 Open
Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2021 workshop at the 15th ACM Conference on Recommender Systems brought together academia and industry to critically …