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Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years.
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Nonlinear Latent Factorization by Embedding Multiple User Interests. In ACM International Conference on Recommender Systems (RecSys)
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Attention is all you need
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Recurrent Recommender Networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM ’17)
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Real-Time Personalization Using Embeddings for Search Ranking at Airbnb. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18)
Mihajlo Grbovic and Haibin Cheng. 2018 · 2018
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Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management
Balázs Hidasi and Alexandros Karatzoglou. 2018 · 2018
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Self-attentive sequential recommendation. In IEEE International Conference on Data Mining (ICDM)
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Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Practice on long sequential user behavior modeling for click-through rate prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM ’19)
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Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Ajit Kumthekar, Zhe Zhao, Li Wei, and Ed Chi (Eds.). 2019 · 2019
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A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM ’19)
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Yu A Malkov and Dmitry A Yashunin. 2018 · 2018
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Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
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Behavior sequence transformer for e-commerce recommendation in alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data
Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019 · 2019
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Time2vec: Learning a vector representation of time
Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, and Marcus Brubaker. 2019 · 2019
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Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM ’19)
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019 · 2019
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Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence
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GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
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PinnerSage
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Mixed negative sampling for learning two-tower neural networks in recommendations. In Companion Proceedings of the Web Conference
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Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-Commerce Search via Embedding Learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York, NY, USA
Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Weipeng Yan, and Wen-Yun Yang. 2020 · 2020
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Top-K Off-Policy Correction for a REINFORCE Recommender System
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed Chi. 2021 · 2021
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