2021

Personalized Embedding-based e-Commerce Recommendations at eBay

Wang, Tian, Brovman, Yuri M., Madhvanath, Sriganesh

Understand

Recommender systems are an essential component of e-commerce marketplaces, helping consumers navigate massive amounts of inventory and find what they need or love.

In this paper, we present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in the same vector space.
In order to alleviate the considerable cold-start problem present in large marketplaces, item and user embeddings are computed using content features and multi-modal onsite user activity respectively.
Data ablation is incorporated into the offline model training process to improve the robustness of the production system.

Built on

Accelerating Large-Scale Inference with Anisotropic Vector Quantization. In International Conference on Machine Learning
Original
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020 · 1908
Earlier work this paper cites.
Content-boosted collaborative filtering for improved recommendations
Prem Melville, Raymond J Mooney, and Ramadass Nagarajan. 2002 · 2002
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on . Ieee, 263–272
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence . AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Factorization machines. In 2010 IEEE International Conference on Data Mining . IEEE, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics . 315–323
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
Earlier work this paper cites.
SVDFeature: a toolkit for feature-based collaborative filtering
Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. 2012 · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Original
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Original
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.

Similar

One-Pass Ranking Models for Low-Latency Product Recommendations. In SIGKDD (Sydney, NSW, Australia). 10
Antonino Freno et al · 2015
Cited alongside, same era.
Session-based recommendations with recurrent neural networks
Original
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Cited alongside, same era.
Recommender Systems: The Textbook (1st ed.)
Charu C. Aggarwal. 2016 · 2016
Cited alongside, same era.
Optimizing Similar Item Recommendations in a Semi-Structured Marketplace to Maximize Conversion. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16) . Association for Computing Machinery, New York, NY, USA, 199–202
Yuri M. Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel Galron, Ryan Snyder, and Paul Wang. 2016 · 2016
Cited alongside, same era.
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Cited alongside, same era.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Cited alongside, same era.
Multi-rate deep learning for temporal recommendation. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . 909–912
Yang Song, Ali Mamdouh Elkahky, and Xiaodong He. 2016 · 2016
Cited alongside, same era.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Billion-scale similarity search with GPUs
Original
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Original
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Deep Item-based Collaborative Filtering for Sparse Implicit Feedback
Original
Daniel A Galron, Yuri M Brovman, Jin Chung, Michal Wieja, and Paul Wang. 2018 · 2018
Cited alongside, same era.

Then

Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Later among the works it cites.
Learning tree-based deep model for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1079–1088
Original
Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018 · 2018
Later among the works it cites.
ONNX: Open Neural Network Exchange
Junjie Bai, Fang Lu, Ke Zhang, et al · 2019
Later among the works it cites.
Complementary Item Recommendations at eBay Scale
Y. M. Brovman. 2019 · 2019
Later among the works it cites.
PyTorch Lightning
WA Falcon. 2019 · 2019
Later among the works it cites.
eBay’s Transformation to a Modern AI Platform
S. Katariya and A. Ramani. 2019 · 2019
Later among the works it cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Later among the works it cites.
Attention-based mixture density recurrent networks for history-based recommendation. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data . 1–9
Tian Wang, Kyunghyun Cho, and Musen Wen. 2019 · 2019
Later among the works it cites.
Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems . 269–277
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019 · 2019
Later among the works it cites.
Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations
Original
Weihao Gao, Xiangjun Fan, Jiankai Sun, Kai Jia, Wenzhi Xiao, Chong Wang, and Xiaobing Liu. 2020 · 2020
Later among the works it cites.
Item-based Collaborative Filtering with BERT. In Proceedings of The 3rd Workshop on e-Commerce and NLP . 54–58
Tian Wang and Yuyangzi Fu. 2020 · 2020
Later among the works it cites.

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