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Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches

, , and . Proceedings of the 13th ACM Conference on Recommender Systems, page 101–109. New York, NY, USA, Association for Computing Machinery, (2019)
DOI: 10.1145/3298689.3347058

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Identifying network failures and evaluating link MTBF from utilization logs., , and . Int. CMG Conference, page 703-710. Computer Measurement Group, (2006)Tutorial: Sequence-Aware Recommender Systems., , and . WWW (Companion Volume), page 1316. ACM, (2019)A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment., , and . Recommender Systems Handbook, Springer, (2011)Consolidation of multi-tier workloads with performance and reliability constraints., , , and . HPCS, page 74-83. IEEE, (2012)Evaluating top-n recommendations "when the best are gone"., , and . RecSys, page 339-342. ACM, (2013)Tutorial on cross-domain recommender systems., and . RecSys, page 401-402. ACM, (2014)User profiling vs. accuracy in recommender system user experience., , and . AVI, page 717-720. ACM, (2012)First International Workshop on User Interfaces for Crowdsourcing and Human Computation., , and . AVI, page 398-400. ACM, (2014)Personalized and Context-Aware TV Program Recommendations Based on Implicit Feedback., , , , and . EC-Web, volume 239 of Lecture Notes in Business Information Processing, page 57-68. Springer, (2015)A unified framework for the bottleneck analysis of multiclass queueing networks., and . Perform. Evaluation, 67 (4): 218-234 (2010)