Understanding choice overload in recommender systems
Abstract
Even though people are attracted by large, high quality recommendation sets, psychological research on choice overload shows that choosing an item from recommendation sets containing many attractive items can be a very difficult task. A web-based user experiment using a matrix factorization algorithm applied to the MovieLens dataset was used to investigate the effect of recommendation set size (5 or 20 items) and set quality (low or high) on perceived variety, recommendation set attractiveness, choice difficulty and satisfaction with the chosen item. The results show that larger sets containing only good items do not necessarily result in higher choice satisfaction compared to smaller sets, as the increased recommendation set attractiveness is counteracted by the increased difficulty of choosing from these sets. These findings were supported by behavioral measurements revealing intensified information search and increased acquisition times for these large attractive sets. Important implications of these findings for the design of recommender system user interfaces will be discussed.
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Recommender systems might account for the cognitive cost of exploring items or learning about them by prioritizing items that are easier for users to evaluate or learn.
“As another example, recommender systems might account for the cognitive cost of exploring items or learning about them by prioritizing items that are easier for users to evaluate or learn 128 .”
From Machine culture (2023) · cites this paper as [128]
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- Authors
- Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M.
- Affiliations
- Eindhoven University of Technology (NL)
- Venue
- Proceedings of the fourth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 63-70
- OpenAlex
- W2044277341
- Cited by
- 232
- References
- 22
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 17 · 2023: 29 · 2022: 14 · 2021: 17 · 2020: 12 · 2019: 13 · 2018: 22 · 2017: 16 · 2016: 22 · 2015: 10 · 2014: 15 · 2013: 12 · 2012: 8
- FWCI
- 6.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion)
- TU/e Research Portal (publishedVersion)
- TU/e Research Portal (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- choice overload, recommender systems, perceived variety, decision difficulty, decision satisfaction, information search, user interface design, matrix factorization, MovieLens
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