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RecSys '19 joint workshop on interfaces and human decision making for recommender systems

Published: 10 September 2019 Publication History

Abstract

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. This workshop will focus on the "human side" of recommender systems research. The workshop goal is to improve users' overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.

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cover image ACM Other conferences
RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems
September 2019
635 pages
ISBN:9781450362436
DOI:10.1145/3298689
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2019

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Author Tags

  1. decision biases
  2. evaluation methods
  3. human computer interaction
  4. human decision making
  5. recommender systems
  6. user interfaces

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RecSys '19
RecSys '19: Thirteenth ACM Conference on Recommender Systems
September 16 - 20, 2019
Copenhagen, Denmark

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RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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