11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'24)
Authors: 
Peter Brusilovsky, 
Marco de Gemmis, 
Alexander Felfernig, 
Marco Polignano, Giovanni Semeraro, 
Martijn WillemsenAuthors Info & Claims
Pages 1253 - 1257
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.
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Index Terms
- 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'24)
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Published In
October 2024
1438 pages
ISBN:9798400705052
DOI:10.1145/3640457
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
New York, NY, United States
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Published: 08 October 2024
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