10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'23).

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

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

1 Citation (Scopus)
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Abstract

Recommender systems (RSs) have undoubtedly played a significant role in addressing the information overload problem by efficiently filtering and suggesting relevant items to users. These systems use both explicit and implicit user preferences to filter available data and suggest items that might align with the user’s interests. This can range from recommending movies on a streaming platform based on previous views to suggesting products for purchase based on browsing history. In their early stages, RSs focused on enhancing their algorithmic capabilities to provide accurate recommendations. However, the overemphasis on algorithms resulted in neglecting the human aspect of the user experience. Recognizing this limitation, recent trends in RSs have started to shift their attention toward incorporating Symbiotic Human-Machines Decision Making models. These models aim to provide users with dynamic and persuasive interfaces that empower them to understand and engage better with the recommendations. This shift represents an essential step in creating recommender systems that truly resonate with users and create a more enjoyable, trustable, and user-friendly experience. A crucial aspect of recommender systems’ evolution lies in their proactive nature. Early works focused on designing systems that could proactively anticipate user preferences and needs. While this remains a valuable trait, modern RSs also recognize the importance of giving users control and transparency over their recommendations. Striking the right balance between proactivity and user control ensures that the system supports users without being overly intrusive, thus enhancing their overall satisfaction. These aspects are the main discussion topics of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’23. In this summary, we introduce the workshop’s motivation and view, review its history, and discuss the most critical issues that deserve attention for future research directions.
Original languageEnglish
Title of host publicationRecSys '23
Subtitle of host publicationProceedings of the 17th ACM Conference on Recommender Systems
EditorsJie Zhang, Li Chen, Shlomo Berkovsky, Min Zhang, Tommaso di Nola, Justin Basilico, Luiz Pizzato, Yang Song
PublisherACM Press
Pages1255-1258
Number of pages4
ISBN (Electronic)979-8-4007-0241-9
DOIs
Publication statusPublished - 14 Sept 2023
Event17th ACM Conference on Recommender Systems, RecSys 2023 - Singapore, Singapore
Duration: 18 Sept 202322 Sept 2023

Conference

Conference17th ACM Conference on Recommender Systems, RecSys 2023
Abbreviated titleRecSys 2023
Country/TerritorySingapore
CitySingapore
Period18/09/2322/09/23

Keywords

  • Decision Biases
  • Evaluation Methods
  • Human Decision Making
  • Human-Computer Interaction
  • Recommender Systems
  • User Interfaces

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