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Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks., , , , , , и . KDD, стр. 596-605. ACM, (2018)Mixture model with multiple centralized retrieval algorithms for result merging in federated search., и . SIGIR, стр. 821-830. ACM, (2012)Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering., , , и . IEEE Trans. Neural Networks, 22 (5): 739-751 (2011)Learning compact hashing codes for efficient tag completion and prediction., , , и . CIKM, стр. 1789-1794. ACM, (2013)Unified utility maximization framework for resource selection., и . CIKM, стр. 32-41. ACM, (2004)Cross-domain and cross-category emotion tagging for comments of online news., , , , , и . SIGIR, стр. 627-636. ACM, (2014)Effect of varying number of documents in blind feedback: analysis of the 2003 NRRC RIA workshop "bf_numdocs" experiment suite., , , и . SIGIR, стр. 476-477. ACM, (2004)Report on the SIGIR 2017 Workshop on eCommerce (ECOM17)., , , , , , , и . SIGIR Forum, 51 (3): 128-138 (2017)The FedLemur project: Federated search in the real world., , , и . J. Assoc. Inf. Sci. Technol., 57 (3): 347-358 (2006)StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding., , , , , , , и . ICLR, OpenReview.net, (2020)