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Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

, , , and . Proceedings of the 10th ACM Conference on Recommender Systems, page 241--248. New York, NY, USA, ACM, (2016)
DOI: 10.1145/2959100.2959167

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On the approximation properties of TP model forms., , and . FUZZ-IEEE, page 1069-1074. IEEE, (2004)Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines., , , and . J. Adv. Comput. Intell. Intell. Informatics, 15 (3): 254-263 (2011)Full-Text Search Engines for Databases., and . Encyclopedia of Artificial Intelligence, IGI Global, (2009)Scalable Collaborative Filtering Approaches for Large Recommender Systems., , , and . J. Mach. Learn. Res., (2009)Fast als-based matrix factorization for explicit and implicit feedback datasets., , and . RecSys, page 71-78. ACM, (2010)Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback., , , and . UMAP Workshops, volume 872 of CEUR Workshop Proceedings, CEUR-WS.org, (2012)Determining Relevant Deep Web Sites by Query Context Identification., and . BIS (Workshops), volume 333 of CEUR Workshop Proceedings, page 119-130. CEUR-WS.org, (2008)A survey on universal approximation and its limits in soft computing techniques., , and . Int. J. Approx. Reason., 33 (2): 185-202 (2003)RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)., , , , , , and . RecSys, page 415-416. ACM, (2016)Computational Complexity Reduction for Factorization-Based Collaborative Filtering Algorithms., and . EC-Web, volume 5692 of Lecture Notes in Computer Science, page 229-239. Springer, (2009)