Letting Users Choose Recommender Algorithms: An Experimental Study

@article{Ekstrand2015LettingUC,
  title={Letting Users Choose Recommender Algorithms: An Experimental Study},
  author={Michael D. Ekstrand and Daniel Kluver and F. M. Harper and J. Konstan},
  journal={Proceedings of the 9th ACM Conference on Recommender Systems},
  year={2015}
}
Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports… Expand
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References

Dynamic generation of personalized hybrid recommender systems