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Randomly-hashed item ids are used ubiquitously in recommendation models.
Videobert: A joint model for video and language representation learning
C. Sun, A. Myers, C. Vondrick, K. Murphy, and C. Schmid · 1904
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Methods and metrics for cold-start recommendations
A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock · 2002
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A music recommendation system with a dynamic k-means clustering algorithm
D. Kim, K.-s. Kim, K.-H. Park, J.-H. Lee, and K. M. Lee · 2007
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Matrix factorization techniques for recommender systems
Y. Koren, R. Bell, and C. Volinsky · 2009
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Feature hashing for large scale multitask learning
K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg · 2009
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Product quantization for nearest neighbor search
H. Jegou, M. Douze, and C. Schmid · 2010
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Wide & deep learning for recommender systems
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, et al · 2016
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Neural discrete representation learning
A. Van Den Oord, O. Vinyals, et al · 2017
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Content-based neighbor models for cold start in recommender systems
M. Volkovs, G. W. Yu, and T. Poutanen · 2017
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Self-attentive sequential recommendation
W. Kang and J. J. McAuley · 2018
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Subword regularization: Improving neural network translation models with multiple subword candidates, 2018
T. Kudo · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
Adaptive feature sampling for recommendation with missing content feature values
S. Shi, M. Zhang, X. Yu, Y. Zhang, B. Hao, Y. Liu, and S. Ma · 2019
Cited alongside, same era.
Jukebox: A generative model for music, 2020
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever · 2020
Cited alongside, same era.
How to learn item representation for cold-start multimedia recommendation?
X. Du, X. Wang, X. He, Z. Li, J. Tang, and T.-S. Chua · 2020
Cited alongside, same era.
Learning multi-granular quantized embeddings for large-vocab categorical features in recommender systems
Soundstream: An end-to-end neural audio codec
N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasacchi · 2021
Later among the works it cites.
Learning vector-quantized item representation for transferable sequential recommenders
Y. Hou, Z. He, J. McAuley, and W. X. Zhao · 2022
Later among the works it cites.
Autoregressive image generation using residual quantization
D. Lee, C. Kim, S. Kim, M. Cho, and W.-S. Han · 2022
Later among the works it cites.
Pinnerformer: Sequence modeling for user representation at pinterest
N. Pancha, A. Zhai, J. Leskovec, and C. Rosenberg · 2022
Later among the works it cites.
Transrec: Learning transferable recommendation from mixture-of-modality feedback
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W.-C. Kang, D. Z. Cheng, T. Chen, X. Yi, D. Lin, L. Hong, and E. H. Chi · 2020
Cited alongside, same era.
Large scale video representation learning via relational graph clustering
H. Lee, J. Lee, J. Y.-H. Ng, and P. Natsev · 2020
Cited alongside, same era.
Neural collaborative filtering vs. matrix factorization revisited, 2020
S. Rendle, W. Krichene, L. Zhang, and J. Anderson · 2020
Cited alongside, same era.
Model size reduction using frequency based double hashing for recommender systems
C. Zhang, Y. Liu, Y. Xie, S. I. Ktena, A. Tejani, A. Gupta, P. K. Myana, D. Dilipkumar, S. Paul, I. Ihara, et al · 2020
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
P. Esser, R. Rombach, and B. Ommer · 2021
Cited alongside, same era.
Learning to embed categorical features without embedding tables for recommendation
W.-C. Kang, D. Z. Cheng, T. Yao, X. Yi, T. Chen, L. Hong, and E. H. Chi · 2021
Cited alongside, same era.
Dropoutnet: Addressing cold start in recommender systems
M. Volkovs, G. Yu, and T. Poutanen
Cited in the paper.
J. Wang, F. Yuan, M. Cheng, J. M. Jose, C. Yu, B. Kong, X. He, Z. Wang, B. Hu, and Z. Li · 2022
Later among the works it cites.
Scaling autoregressive models for content-rich text-to-image generation
J. Yu, Y. Xu, J. Y. Koh, T. Luong, G. Baid, Z. Wang, V. Vasudevan, A. Ku, Y. Yang, B. K. Ayan, et al · 2022
Later among the works it cites.
A content-driven micro-video recommendation dataset at scale
Y. Ni, Y. Cheng, X. Liu, J. Fu, Y. Li, X. He, Y. Zhang, and F. Yuan · 2023
Closest in time.
Recommender systems with generative retrieval
S. Rajput, N. Mehta, A. Singh, R. Keshavan, T. Vu, L. Heldt, L. Hong, Y. Tay, V. Q. Tran, J. Samost, and M. Sathiamoorthy · 2023
Closest in time.
Improving training stability for multitask ranking models in recommender systems
J. Tang, Y. Drori, D. Chang, M. Sathiamoorthy, J. Gilmer, L. Wei, X. Yi, L. Hong, and E. H. Chi · 2023
Closest in time.
Where to go next for recommender systems? id- vs. modality-based recommender models revisited, 2023
Z. Yuan, F. Yuan, Y. Song, Y. Li, J. Fu, F. Yang, Y. Pan, and Y. Ni · 2023
Closest in time.