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.
- RecSys152015
2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 138 times. Cited 100 times.
, , , and . - ⸘2014‽2014
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.
. - RecSys142014
2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14). ACM. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 283 times. Cited 186 times.
, , , and . - RecSys12-f2012
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, pp. 233–236. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 88 times. Cited 73 times.
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