Home Mathematics 2 User-centered recommender systems
Chapter
Licensed
Unlicensed Requires Authentication

2 User-centered recommender systems

  • Jürgen Ziegler and Benedikt Loepp
Become an author with De Gruyter Brill
Explore this Subject How to publish with us
Personalized Human-Computer Interaction
This chapter is in the book Personalized Human-Computer Interaction

Abstract

Recommender systems aim at facilitating users’ search and decision-making when they are faced with a large number of available options, such as buying products online or selecting music tracks to listen to. A broad range of machine learning models and algorithms has been developed that aim at predicting users’ assessment of unseen items and at recommending items that best match their interests. However, it has been shown that optimizing the system in terms of algorithm accuracy often does not result in a correspondingly high level of user satisfaction. Therefore, a more user-centric approach to developing recommender systems is needed that better takes into account users’ actual goals, the current context and their cognitive demands. In this chapter, we discuss a number of techniques and design aspects that can contribute to increasing transparency, user understanding and interactive control of recommender systems. Furthermore, we present methods for evaluating systems from a user perspective and point out future research directions.

Abstract

Recommender systems aim at facilitating users’ search and decision-making when they are faced with a large number of available options, such as buying products online or selecting music tracks to listen to. A broad range of machine learning models and algorithms has been developed that aim at predicting users’ assessment of unseen items and at recommending items that best match their interests. However, it has been shown that optimizing the system in terms of algorithm accuracy often does not result in a correspondingly high level of user satisfaction. Therefore, a more user-centric approach to developing recommender systems is needed that better takes into account users’ actual goals, the current context and their cognitive demands. In this chapter, we discuss a number of techniques and design aspects that can contribute to increasing transparency, user understanding and interactive control of recommender systems. Furthermore, we present methods for evaluating systems from a user perspective and point out future research directions.

Chapters in this book

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of personalization
  6. 1 Theory-grounded user modeling for personalized HCI 1
  7. 2 User-centered recommender systems 33
  8. 3 Fairness of information access systems 59
  9. Part II: User input and feedback
  10. 4 Personalization and user modeling for interaction processes 81
  11. 5 How to use socio-emotional signals for adaptive training 99
  12. 6 Explanations and user control in recommender systems 129
  13. 7 Feedback loops and mutual reinforcement in personalized interaction 153
  14. Part III: Personalization purposes and goals
  15. 8 Personalizing the user interface for people with disabilities 175
  16. 9 Personalized persuasion for behavior change 205
  17. 10 Personalization approaches for remote collaborative interaction 237
  18. Part IV: Personalization domains
  19. 11 Listener awareness in music recommender systems: directions and current trends 279
  20. 12 Tourist trip recommendations – foundations, state of the art and challenges 313
  21. 13 Pictures as a tool for matching tourist preferences with destinations 337
  22. Index 355
https://doi.org/10.1515/9783110988567-002

Chapters in this book

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of personalization
  6. 1 Theory-grounded user modeling for personalized HCI 1
  7. 2 User-centered recommender systems 33
  8. 3 Fairness of information access systems 59
  9. Part II: User input and feedback
  10. 4 Personalization and user modeling for interaction processes 81
  11. 5 How to use socio-emotional signals for adaptive training 99
  12. 6 Explanations and user control in recommender systems 129
  13. 7 Feedback loops and mutual reinforcement in personalized interaction 153
  14. Part III: Personalization purposes and goals
  15. 8 Personalizing the user interface for people with disabilities 175
  16. 9 Personalized persuasion for behavior change 205
  17. 10 Personalization approaches for remote collaborative interaction 237
  18. Part IV: Personalization domains
  19. 11 Listener awareness in music recommender systems: directions and current trends 279
  20. 12 Tourist trip recommendations – foundations, state of the art and challenges 313
  21. 13 Pictures as a tool for matching tourist preferences with destinations 337
  22. Index 355
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 12.12.2025 from https://www.degruyterbrill.com/document/doi/10.1515/9783110988567-002/html
Scroll to top button