Inspectability and control in social recommenders

@inproceedings{Knijnenburg2012InspectabilityAC,
  title={Inspectability and control in social recommenders},
  author={Bart P. Knijnenburg and Svetlin Bostandjiev and J. O'Donovan and A. Kobsa},
  booktitle={RecSys '12},
  year={2012}
}
Users of social recommender systems may want to inspect and control how their social relationships influence the recommendations they receive, especially since recommendations of social recommenders are based on friends rather than anonymous "nearest neighbors. [...] Key Result The results show that inspectability and control indeed increase users' perceived understanding of and control over the system, their rating of the recommendation quality, and their satisfaction with the system.Expand
110 Citations

Figures and Topics from this paper

User Control in Recommender Systems: Overview and Interaction Challenges
  • 22
Testing a recommender system for self-actualization
  • 4
  • PDF
Providing Control and Transparency in a Social Recommender System for Academic Conferences
  • 17
  • PDF
User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis
  • 1
  • PDF
Explaining recommendations in an interactive hybrid social recommender
  • 17
  • Highly Influenced
Hypothetical Recommendation: A Study of Interactive Profile Manipulation Behavior for Recommender Systems
  • 19
  • PDF
Displaying User Profiles to Elicit User Awareness in Recommender Systems
  • Y. Hijikata, K. Okubo, S. Nishida
  • Computer Science
  • 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
  • 2015
  • 4
Interacting with Recommenders—Overview and Research Directions
  • 66
...
1
2
3
4
5
...

References

SHOWING 1-6 OF 6 REFERENCES
Comparing Recommendations Made by Online Systems and Friends
  • 496
  • Highly Influential
  • PDF
Designing and Evaluating Explanations for Recommender Systems
  • 272
  • Highly Influential
  • PDF
Evaluating the effectiveness of explanations for recommender systems
  • 204
  • Highly Influential
  • PDF
Explaining collaborative filtering recommendations
  • 1,525
  • Highly Influential
  • PDF
Bringing Scrutability to Adaptive Hypertext Teaching
  • 20
  • Highly Influential
Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives
  • 57,968
  • Highly Influential