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Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems.
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Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 5941–5948
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DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-Scale Learning to Rank Systems. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 1785–1797
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3 Innovations While Unifying Pinterest’s Key-Value Storage
Jessica Chan. 2022 · 2022
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PinnerFormer: Sequence Modeling for User Representation at Pinterest. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22) . Association for Computing Machinery, New York, NY, USA, 3702–3712
Nikil Pancha, Andrew Zhai, Jure Leskovec, and Charles Rosenberg. 2022 · 2022
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Surrogate for Long-Term User Experience in Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22) . Association for Computing Machinery, New York, NY, USA, 4100–4109
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Rethinking Personalized Ranking at Pinterest: An End-to-End Approach. In Proceedings of the 16th ACM Conference on Recommender Systems (Seattle, WA, USA) (RecSys ’22) . Association for Computing Machinery, New York, NY, USA, 502–505
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