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11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’24)

  • Peter Brusilovsky
  • , Marco de Gemmis
  • , Alexander Felfernig
  • , Marco Polignano
  • , Giovanni Semeraro
  • , Martijn C. Willemsen

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

The primary goal of Recommender Systems is to suggest the most suitable items to a user, aligning them with the user’s interests and needs. RSs are essential for modern e-commerce, helping users discover content and products by predicting suitable items based on their past behavior. However, their success isn’t just about advanced algorithms. The design of the user interface and a good integration with the human decision-making process are equally crucial. A well-designed interface enhances the user experience and makes recommendations more effective, while a poor interface can lead to frustration. Recognizing this limitation, recent trends in Recommender Systems (RSs) are increasingly focusing on integrating Symbiotic Human-Machine Decision-Making models. These models aim to offer users a dynamic and persuasive interface that helps them better understand and engage with recommendations. This shift is a crucial step toward developing recommender systems that truly connect with users and offer a more enjoyable, trustworthy, explainable, and user-friendly experience. Although early efforts concentrated on creating systems that could proactively predict user preferences and needs, modern RSs also emphasize the importance of providing users with control and transparency over their recommendations. Finding the right balance between proactivity and user control is essential to ensure that the system supports users without being too intrusive, thus improving their overall satisfaction. As Large Language Models (LLMs) become more integrated into recommender systems, the importance of user-centric interfaces and a deep understanding of decision-making becomes even more critical. Effective integration of LLMs requires interfaces that are both visually and cognitively engaging. These aspects are the main discussion topics of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’24. In this summary, we introduce the motivation and perspective of the workshop, review its history, and discuss the most critical issues that deserve attention for future research directions.

Original languageEnglish
Title of host publicationRecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery (ACM)
Pages1253-1257
Number of pages5
ISBN (Electronic)9798400705052
DOIs
Publication statusPublished - 8 Oct 2024
Event18th ACM Conference on Recommender Systems, RecSys 2024 - Bari, Italy
Duration: 14 Oct 202418 Oct 2024
https://recsys.acm.org/recsys24/

Publication series

NameRecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems

Conference

Conference18th ACM Conference on Recommender Systems, RecSys 2024
Abbreviated titleRecSys '24
Country/TerritoryItaly
CityBari
Period14/10/2418/10/24
Internet address

Keywords

  • Decision Biases
  • Evaluation Methods
  • Human Decision Making
  • Human-Computer Interaction
  • Large Language Models
  • LLMs
  • Recommender Systems
  • Symbiotic-AI
  • User Interfaces

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Software
  • Control and Systems Engineering

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