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TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations.

, , , and . RecSys, page 338-347. ACM, (2020)

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Improving Non-Autoregressive Translation Models Without Distillation., , and . ICLR, OpenReview.net, (2022)MCL: Mixed-Centric Loss for Collaborative Filtering., , , , and . WWW, page 2339-2347. ACM, (2022)DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation., , , and . ACL (Findings), page 8538-8553. Association for Computational Linguistics, (2023)TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations., , , and . RecSys, page 338-347. ACM, (2020)HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering., , , , and . WWW, page 593-601. ACM / IW3C2, (2021)User Engagement Modeling with Deep Learning and Language Models., , , , , , , and . RecSys Challenge, page 22-27. ACM, (2021)Robust contextual models for in-session personalization., , , , , and . RecSys Challenge, page 2:1-2:5. ACM, (2019)Improving Transformer Optimization Through Better Initialization., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 4475-4483. PMLR, (2020)DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers., , , and . CoRR, (2022)Pre-Processing Techniques for Improving a Topological Map-Matching Algorithm., , , and . SCCC, page 1-6. IEEE, (2022)