Differences in Recommender Algorithms

In this line of work, I have been trying to understand what is different about the output of various collaborative filtering techniques, particularly as such differences relate to the user’s perception of the recommendations and the ability of the recommender to meet their information needs.

• RecSys15

  2015

  Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15), Sep 16, 2015. ACM, pp. 11–18. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 138 times. Cited 101 times.

• ⸘2014‽

  2014

  Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D thesis, University of Minnesota. HDL 11299/165307. Cited 8 times. Cited 4 times.

• RecSys14

  2014

  Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14), Oct 6, 2014. ACM, pp. 161–168. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 294 times. Cited 192 times.

• RecSys12f

  2012

  Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination. Short paper in Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12), Sep 10, 2012. ACM, pp. 233–236. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 90 times. Cited 73 times.