Abstract
Recommender systems have shown to be valuable tools that help users find items of interest in situations of information overload. These systems usually predict the relevance of each item for the individual user based on their past preferences and their observed behavior. If the system’s assumption about the users’ preferences are however incorrect or outdated, mechanisms should be provided that put the user into control of the recommendations, e.g., by letting them specify their preferences explicitly or by allowing them to give feedback on the recommendations. In this paper we review and classify the different approaches from the research literature of putting the users into active control of what is recommended. We highlight the challenges related to the design of the corresponding user interaction mechanisms and finally present the results of a survey-based study in which we gathered user feedback on the implemented user control features on Amazon.
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Notes
- 1.
In some rating-based systems users can update their ratings, which might however be tedious, and changes often have no immediate effect on the presented recommendations.
- 2.
A translated version of the survey forms can be found at
http://ls13-www.cs.tu-dortmund.de/homepage/publications/ec-web-2016/.
- 3.
The participants could provide several reasons and the value 65% indicates that nearly two thirds of the users stated that the recommendations were inadequate.
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Jannach, D., Naveed, S., Jugovac, M. (2017). User Control in Recommender Systems: Overview and Interaction Challenges. In: Bridge, D., Stuckenschmidt, H. (eds) E-Commerce and Web Technologies. EC-Web 2016. Lecture Notes in Business Information Processing, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-319-53676-7_2
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