5. Explanations and user control in recommender systems
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Dietmar Jannach
Abstract
Adaptive, personalized recommendations have become a common feature of today’s web and mobile app user interfaces. In most of modern applications, however, the underlying recommender systems are black boxes for the users, and no detailed information is provided about why certain items were selected for recommendation. Users also often have very limited means to influence (e. g., correct) the provided suggestions and to apply information filters. This can potentially lead to a limited acceptance of the recommendation system. In this chapter, we review explanations and feedback mechanisms as a means of building trustworthy recommender and advice giving systems that put their users in control of the personalization process, and we outline existing challenges in the area.
Abstract
Adaptive, personalized recommendations have become a common feature of today’s web and mobile app user interfaces. In most of modern applications, however, the underlying recommender systems are black boxes for the users, and no detailed information is provided about why certain items were selected for recommendation. Users also often have very limited means to influence (e. g., correct) the provided suggestions and to apply information filters. This can potentially lead to a limited acceptance of the recommendation system. In this chapter, we review explanations and feedback mechanisms as a means of building trustworthy recommender and advice giving systems that put their users in control of the personalization process, and we outline existing challenges in the area.
Chapters in this book
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
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Part I: Foundations of user modeling
- 1. Theory-grounded user modeling for personalized HCI 1
- 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
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Part II: User input and feedback
- 3. Automated personalization of input methods and processes 67
- 4. How to use socio-emotional signals for adaptive training 103
- 5. Explanations and user control in recommender systems 133
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Part III: Personalization approaches
- 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
- 7. Pictures as a tool for matching tourist preferences with destinations 183
- 8. Towards personalized virtual reality touring through cross-object user interfaces 201
- 9. User awareness in music recommender systems 223
- 10. Personalizing the user interface for people with disabilities 253
- 11. Adaptive workplace learning assistance 283
- Index 303
Chapters in this book
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
-
Part I: Foundations of user modeling
- 1. Theory-grounded user modeling for personalized HCI 1
- 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
-
Part II: User input and feedback
- 3. Automated personalization of input methods and processes 67
- 4. How to use socio-emotional signals for adaptive training 103
- 5. Explanations and user control in recommender systems 133
-
Part III: Personalization approaches
- 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
- 7. Pictures as a tool for matching tourist preferences with destinations 183
- 8. Towards personalized virtual reality touring through cross-object user interfaces 201
- 9. User awareness in music recommender systems 223
- 10. Personalizing the user interface for people with disabilities 253
- 11. Adaptive workplace learning assistance 283
- Index 303
