ABSTRACT
The embedding index has become an essential part of the dense retrieval (DR) system, which enables a fast search for billion of items in online E-commerce applications. To accelerate the retrieval process in industrial scenarios, most of the previous studies only utilize item embeddings. However, the product quantization process without query embeddings will lead to inconsistency between queries and items. A straightforward solution is to put query embedding into the product quantization process. But we found that the distance of the positive query and item embedding pairs is too large, which means the query and item embeddings learned by the two-tower are not fully aligned. This problem would lead to performance decay when directly putting query embeddings into the product quantization.
In this paper, we propose a novel query-aware embedding Index framework, which aligns the query and item embedding space to reduce the distance between positive pairs, thereby mixing the query and item embeddings to learn better cluster centers for product quantization. Specifically, we first propose s symmetric loss to train a better two-tower to achieve space alignment. Subsequently, we propose a mixed quantization strategy to put the query embeddings into the product quantization process for bridging the gap between queries and compressed item embeddings. Extensive experiments show that our framework significantly outperforms previous models on a real-world dataset, which demonstrates the superiority and effectiveness of the framework.
- Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828.Google ScholarDigital Library
- Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the twentieth annual symposium on Computational geometry. 253--262.Google ScholarDigital Library
- Jeffrey Dean. 2009. Challenges in building large-scale information retrieval systems. In Keynote of the 2nd ACM International Conference on Web Search and Data Mining (WSDM), Vol. 10.Google ScholarDigital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun. 2013. Optimized product quantization. IEEE transactions on pattern analysis and machine intelligence, Vol. 36, 4 (2013), 744--755.Google Scholar
- Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020. Accelerating large-scale inference with anisotropic vector quantization. In International Conference on Machine Learning. PMLR, 3887--3896.Google Scholar
- Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin, and Allan Hanbury. 2021. Efficiently teaching an effective dense retriever with balanced topic aware sampling. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 113--122.Google ScholarDigital Library
- Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang. 2020. Embedding-based retrieval in facebook search. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2553--2561.Google ScholarDigital Library
- Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333--2338.Google ScholarDigital Library
- Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2010. Product quantization for nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence, Vol. 33, 1 (2010), 117--128.Google Scholar
- Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019. Billion-scale similarity search with gpus. IEEE Transactions on Big Data, Vol. 7, 3 (2019), 535--547.Google ScholarCross Ref
- Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 6769--6781.Google ScholarCross Ref
- Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT. 4171--4186.Google Scholar
- Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 39--48.Google ScholarDigital Library
- Sen Li, Fuyu Lv, Taiwei Jin, Guli Lin, Keping Yang, Xiaoyi Zeng, Xiao-Ming Wu, and Qianli Ma. 2021. Embedding-based Product Retrieval in Taobao Search. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3181--3189.Google ScholarDigital Library
- Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. 2020. Distilling dense representations for ranking using tightly-coupled teachers. arXiv preprint arXiv:2010.11386 (2020).Google Scholar
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).Google Scholar
- Yiming Qiu, Chenyu Zhao, Han Zhang, Jingwei Zhuo, Tianhao Li, Xiaowei Zhang, Songlin Wang, Sulong Xu, Bo Long, and Wen-Yun Yang. 2022. Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4424--4428.Google ScholarDigital Library
- Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021. RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5835--5847.Google ScholarCross Ref
- Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021. PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2173--2183.Google Scholar
- Stephen E Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR'94. Springer, 232--241.Google ScholarCross Ref
- Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural networks, Vol. 61 (2015), 85--117.Google Scholar
- Feng Wang and Huaping Liu. 2021. Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2495--2504.Google ScholarCross Ref
- Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N Bennett, Junaid Ahmed, and Arnold Overwijk. 2020. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In International Conference on Learning Representations.Google Scholar
- Chunyuan Yuan, Yiming Qiu, Mingming Li, Haiqing Hu, Songlin Wang, and Sulong Xu. 2023. A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval. arXiv preprint arXiv:2303.15870 (2023).Google Scholar
- Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2021. Optimizing dense retrieval model training with hard negatives. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1503--1512.Google ScholarDigital Library
- Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2022. Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1328--1336.Google ScholarDigital Library
- Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020. Repbert: Contextualized text embeddings for first-stage retrieval. arXiv preprint arXiv:2006.15498 (2020).Google Scholar
- Han Zhang, Hongwei Shen, Yiming Qiu, Yunjiang Jiang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, and Wen-Yun Yang. 2021. Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1718--1722.Google ScholarDigital Library
- Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Weipeng Yan, and Wen-Yun Yang. 2020. 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. 2407--2416.Google ScholarDigital Library
Index Terms
- Learning Query-aware Embedding Index for Improving E-commerce Dense Retrieval
Recommendations
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementDense retrieval systems conduct first-stage retrieval using embedded representations and simple similarity metrics to match a query to documents. Its effectiveness depends on encoded embeddings to capture the semantics of queries and documents, a ...
Query Embedding Pruning for Dense Retrieval
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementRecent advances in dense retrieval techniques have offered the promise of being able not just to re-rank documents using contextualised language models such as BERT, but also to use such models to identify documents from the collection in the first ...
More Robust Dense Retrieval with Contrastive Dual Learning
ICTIR '21: Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information RetrievalDense retrieval conducts text retrieval in the embedding space and has shown many advantages compared to sparse retrieval. Existing dense retrievers optimize representations of queries and documents with contrastive training and map them to the ...
Comments