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.
2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the Ninth ACM Conference on Recommender Systems (RecSys '15). ACM. DOI:10.1145/2792838.2800195. Acceptance rate: 21%., , , and .
Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D Thesis, University of Minnesota.
2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the Eighth ACM Conference on Recommender Systems (RecSys '14). ACM. DOI:10.1145/2645710.2645737. Acceptance rate: 23%., , , and .
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). ACM, 233–236. DOI:10.1145/2365952.2366002. Acceptance rate: 32%.and .