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Embedding-based neural retrieval is a prevalent approach to address the semantic gap problem which often arises in product search on tail queries.
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2013
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Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil, “A latent semantic model with convolutional-pooling structure for information retrieval,” in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management , ser. CIKM ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 101–110
2014
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L. Pang, Y. Lan, J. Guo, J. Xu, S. Wan, and X. Cheng, “Text matching as image recognition,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence , ser. AAAI’16. AAAI Press, 2016, p. 2793–2799
2016
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S. Wan, Y. Lan, J. Xu, J. Guo, L. Pang, and X. Cheng, “Match-srnn: Modeling the recursive matching structure with spatial rnn,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence , ser. IJCAI’16. AAAI Press, 2016, p. 2922–2928
2016
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F. André, A.-M. Kermarrec, and N. Le Scouarnec, Cache locality is not enough: High-Performance nearest neighbor search with product quantization fast scan , 01 2016, pp. 288–299
2016
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W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. Red Hook, NY, USA: Curran Associates Inc., 2017, p. 1025–1035
2017
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S. K. Karmaker Santu, P. Sondhi, and C. Zhai, “On application of learning to rank for e-commerce search,” in Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval , 2017, pp. 475–484
2017
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J. Johnson, M. Douze, and H. Jégou, “Billion-scale similarity search with gpus,” IEEE Transactions on Big Data , vol. 7, pp. 535–547, 2017
2017
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D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley, “Google vizier: A service for black-box optimization,” in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , 2017, pp. 1487–1495
2017
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P. Nigam, Y. Song, V. Mohan, V. Lakshman, W. A. Ding, A. Shingavi, C. H. Teo, H. Gu, and B. Yin, “Semantic product search,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , ser. KDD ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 2876–2885. [Online]. Available: https://doi.org/10.1145/3292500.3330759
2019
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J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of naacL-HLT , vol. 1, 2019, p. 2
2019
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Q. Ai, D. N. Hill, S. Vishwanathan, and W. B. Croft, “A zero attention model for personalized product search,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , 2019, pp. 379–388
2019
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X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 165–174
2019
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Y. Zhang, D. Wang, and Y. Zhang, “Neural ir meets graph embedding: A ranking model for product search,” in The World Wide Web Conference , 2019, pp. 2390–2400
2019
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2019
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D. R. Cheriton, “From doc2query to doctttttquery,” 2019
2019
Cited alongside, same era.
L. Gao, Z. Dai, T. Chen, Z. Fan, B. Van Durme, and J. Callan, “Complement lexical retrieval model with semantic residual embeddings,” in Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28–April 1, 2021, Proceedings, Part I 43 . Springer, 2021, pp. 146–160
2021
Later among the works it cites.
S. Li, F. Lv, T. Jin, G. Lin, K. Yang, X. Zeng, X.-M. Wu, and Q. Ma, “Embedding-based product retrieval in taobao search,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , ser. KDD ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 3181–3189. [Online]. Available: https://doi.org/10.1145/3447548.3467101
2021
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H. Lu, Y. Hu, T. Zhao, T. Wu, Y. Song, and B. Yin, “Graph-based multilingual product retrieval in e-commerce search,” in NAACL 2021 , 2021
2021
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J.-T. Huang, A. Sharma, S. Sun, L. Xia, D. Zhang, P. Pronin, J. Padmanabhan, G. Ottaviano, and L. Yang, “Embedding-based retrieval in facebook search,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , ser. KDD ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 2553–2561
2020
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C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. Mach. Learn. Res. , vol. 21, no. 1, jan 2020
2020
Cited alongside, same era.
K. Bi, Q. Ai, and W. B. Croft, “A transformer-based embedding model for personalized product search,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2020, pp. 1521–1524
2020
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H. Zhang, S. Wang, K. Zhang, Z. Tang, Y. Jiang, Y. Xiao, W. Yan, and W.-Y. Yang, “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 , ser. SIGIR ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 2407–2416
2020
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Z. Jiang, A. El-Jaroudi, W. Hartmann, D. Karakos, and L. Zhao, “Cross-lingual information retrieval with BERT,” in Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020) . Marseille, France: European Language Resources Association, May 2020, pp. 26–31
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J.-T. Huang, A. Sharma, S. Sun, L. Xia, D. Zhang, P. Pronin, J. Padmanabhan, G. Ottaviano, and L. Yang, “Embedding-based retrieval in facebook search,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 2553–2561
2020
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Y. A. Malkov and D. A. Yashunin, “Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 4, p. 824–836, apr 2020
2020
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L. Zhuang, L. Wayne, S. Ya, and Z. Jun, “A robustly optimized BERT pre-training approach with post-training,” in Proceedings of the 20th Chinese National Conference on Computational Linguistics . Huhhot, China: Chinese Information Processing Society of China, Aug. 2021, pp. 1218–1227
2021
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W.-C. Chang, D. Jiang, H.-F. Yu, C. H. Teo, J. Zhang, K. Zhong, K. Kolluri, Q. Hu, N. Shandilya, V. Ievgrafov et al. , “Extreme multi-label learning for semantic matching in product search,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , 2021, pp. 2643–2651
2021
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J. Zhan, J. Mao, Y. Liu, J. Guo, M. Zhang, and S. Ma, “Optimizing dense retrieval model training with hard negatives,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 1503–1512
2021
Later among the works it cites.
W. Zhang and K. Stratos, “Understanding hard negatives in noise contrastive estimation,” in North American Chapter of the Association for Computational Linguistics , 2021
2021
Later among the works it cites.
J. Zhan, J. Mao, Y. Liu, J. Guo, M. Zhang, and S. Ma, “Optimizing dense retrieval model training with hard negatives,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, pp. 1503–1512
2021
Later among the works it cites.
Y. Xie, T. Na, X. Xiao, S. Manchanda, Y. Rao, Z. Xu, G. Shu, E. Vasiete, T. Tenneti, and H. Wang, “An embedding-based grocery search model at instacart,” 2022
2022
Later among the works it cites.
A. Magnani, F. Liu, S. Chaidaroon, S. Yadav, P. Reddy Suram, A. Puthenputhussery, S. Chen, M. Xie, A. Kashi, T. Lee, and C. Liao, “Semantic retrieval at walmart,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 3495–3503
2022
Later among the works it cites.
“Scikit-optimize: Sequential model-based optimization in python,” 2023. [Online]. Available: https://scikit-optimize.github.io
2023
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