User perception of differences in recommender algorithms

@inproceedings{Ekstrand2014UserPO,
  title={User perception of differences in recommender algorithms},
  author={Michael D. Ekstrand and F. M. Harper and M. Willemsen and J. Konstan},
  booktitle={RecSys '14},
  year={2014}
}
Recent developments in user evaluation of recommender systems have brought forth powerful new tools for understanding what makes recommendations effective and useful. We apply these methods to understand how users evaluate recommendation lists for the purpose of selecting an algorithm for finding movies. This paper reports on an experiment in which we asked users to compare lists produced by three common collaborative filtering algorithms on the dimensions of novelty, diversity, accuracy… Expand
Letting Users Choose Recommender Algorithms: An Experimental Study
Putting Users in Control of their Recommendations
Towards Recommender Engineering: tools and experiments for identifying recommender differences
Item Familiarity Effects in User-Centric Evaluations of Recommender Systems
User Personality and User Satisfaction with Recommender Systems
Personalized Recommendations for Music Genre Exploration
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
...
1
2
3
4
5
...

References

SHOWING 1-2 OF 2 REFERENCES
Netflix Update: Try This at Home
  • The Evolution of Cybernetics. 2006. : http://sifter.org/ ~simon/journal/20061211.html
  • 2010