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.

• 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 137 times. Cited 104 times.

• 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.

• 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 304 times. Cited 195 times.

• 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 88 times. Cited 73 times.