Explaining the user experience of recommender systems

Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework link...

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Vydáno v:User modeling and user-adapted interaction Ročník 22; číslo 4-5; s. 441 - 504
Hlavní autoři: Knijnenburg, Bart P., Willemsen, Martijn C., Gantner, Zeno, Soncu, Hakan, Newell, Chris
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.10.2012
Springer Nature B.V
Témata:
Accuracy
Algorithms
Computer Science
Control systems
Decision support systems
Electronic commerce
Feedback
Insurance policies
Management of Computing and Information Systems
Mathematical analysis
Mathematical models
Multimedia Information Systems
Original Paper
Perception
Perceptions
Platforms
Preferences
Privacy
R&D
Recommender systems
Research & development
Studies
System effectiveness
User behavior
User experience
User interface
User Interfaces and Human Computer Interaction
Decision support systems
Decision-making
Privacy
User testing
Preference elicitation
Human-computer interaction
User experience
User-centric evaluation
Recommender systems
ISSN:0924-1868, 1573-1391
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Shrnutí:Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated with four field trials and two controlled experiments and analyzed using Structural Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about . In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.
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ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-011-9118-4