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Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding.
Locality-preserving hashing in multidimensional spaces
Piotr Indyk, Rajeev Motwani, Prabhakar Raghavan, and Santosh Vempala · 1997
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Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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Similarity estimation techniques from rounding algorithms
Moses S Charikar · 2002
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A music recommendation system with a dynamic k-means clustering algorithm
Dongmoon Kim, Kun-su Kim, Kyo-Hyun Park, Jee-Hyong Lee, and Keon Myung Lee · 2007
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Feature hashing for large scale multitask learning
Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg · 2009
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Product quantization for nearest neighbor search
Herve Jegou, Matthijs Douze, and Cordelia Schmid · 2010
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The netflix recommender system: Algorithms, business value, and innovation
Carlos A Gomez-Uribe and Neil Hunt · 2015
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Session-based recommendations with recurrent neural networks
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
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Neural machine translation of rare words with subword units
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Neural attentive session-based recommendation
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Tim Kraska, Alex Beutel, Ed H Chi, Jeffrey Dean, and Neoklis Polyzotis · 2018
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Soundstream: An end-to-end neural audio codec
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Sampling-bias-corrected neural modeling for large corpus item recommendations
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Feature-level deeper self-attention network for sequential recommendation
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Transformer memory as a differentiable search index
Yi Tay, Vinh Q Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, et al · 2022
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Better generalization with semantic ids: A case study in ranking for recommendations
Anima Singh, Trung Vu, Raghunandan Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed Chi, and Maheswaran Sathiamoorthy · 2023
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