Ying Li
Jizhou Huang
Enhong Chen
Abstract
Query auto-completion (QAC) is a featured function that has been widely adopted by many sub-domains of search. It can dramatically reduce the number of typed characters and avoid spelling mistakes. These merits of QAC are highlighted to improve user satisfaction, especially when users intend to type in a query on mobile devices. In this article, we will present our industrial solution to the personalized QAC for the point of interest (POI) search at Baidu Maps, a well-known Web mapping service on mobiles in China. The industrial solution makes a good tradeoff between the offline effectiveness of a novel neural learning model that we devised for feature generation and the online efficiency of an off-the-shelf learning to rank (LTR) approach for the real-time suggestion. Besides some practical lessons from how a real-world QAC system is built and deployed in Baidu Maps to facilitate a large number of users in searching tens of millions of POIs, we mainly explore two specific features for the personalized QAC function of the POI search engine: the spatial-temporal characteristics of POIs and the historically queried POIs of individual users.
We leverage the large-volume POI search logs in Baidu Maps to conduct offline evaluations of our personalized QAC model measured by multiple metrics, including Mean Reciprocal Rank (MRR), Success Rate (SR), and normalized Discounted Cumulative Gain (nDCG). Extensive experimental results demonstrate that the personalized model enhanced by the proposed features can achieve substantial improvements (i.e., +3.29% MRR, +3.78% SR@1, +5.17% SR@3, +1.96% SR@5, and +3.62% nDCG@5). After deploying this upgraded model into the POI search engine at Baidu Maps for A/B testing online, we observe that some other critical indicators, such as the average number of keystrokes and the average typing speed at keystrokes in a QAC session, which are also related to user satisfaction, decrease as well by 1.37% and 1.69%, respectively. So the conclusion is that the two kinds of features contributed by us are quite helpful in personalized mapping services for industrial practice.
- Nima Asadi and Jimmy Lin. 2012. Fast candidate generation for two-phase document ranking: Postings list intersection with bloom filters. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12). ACM, New York, NY, 2419--2422. DOI:https://doi.org/10.1145/2396761.2398656Google Scholar
Digital Library
- Ziv Bar-Yossef and Naama Kraus. 2011. Context-sensitive query auto-completion. In Proceedings of the 20th International Conference on World Wide Web (WWW’11). ACM, New York, NY, 107--116. DOI:https://doi.org/10.1145/1963405.1963424Google ScholarDigital Library
- Y. Bengio, A. Courville, and P. Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 8 (Aug 2013), 1798--1828. DOI:https://doi.org/10.1109/TPAMI.2013.50Google ScholarDigital Library
- Andrei Z. Broder, David Carmel, Michael Herscovici, Aya Soffer, and Jason Zien. 2003. Efficient query evaluation using a two-level retrieval process. In Proceedings of the 12th International Conference on Information and Knowledge Management (CIKM’03). ACM, New York, NY, 426--434. DOI:https://doi.org/10.1145/956863.956944Google ScholarDigital Library
- Fei Cai and Maarten de Rijke. 2016. A survey of query auto completion in information retrieval. Found. Trends Inf. Retr. 10, 4 (Sept. 2016), 273–363. DOI:https://doi.org/10.1561/1500000055Google ScholarDigital Library
- Fei Cai, Shangsong Liang, and Maarten de Rijke. 2014. Time-sensitive personalized query auto-completion. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM’14). ACM, New York, NY, 1599--1608. DOI:https://doi.org/10.1145/2661829.2661921Google ScholarDigital Library
- Fei Cai, Ridho Reinanda, and Maarten De Rijke. 2016. Diversifying query auto-completion. ACM Transactions on Information Systems (TOIS) 34, 4, Article 25 (June 2016), 33 pages. DOI:https://doi.org/10.1145/2910579Google ScholarDigital Library
- Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder--decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). Association for Computational Linguistics, Doha, Qatar, 1724--1734. DOI:https://doi.org/10.3115/v1/D14-1179Google ScholarCross Ref
- V. S. Dandagi and N. Sidnal. 2018. Review on query auto-completion. In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems. 119--123.Google Scholar
- Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, and Xueqi Cheng. 2019. A Deep Look into Neural Ranking Models for Information Retrieval. arxiv:cs.IR/1903.06902Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- Aaron Jaech and Mari Ostendorf. 2018. Personalized language model for query auto-completion. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Melbourne, Australia, 700--705. DOI:https://doi.org/10.18653/v1/P18-2111Google ScholarCross Ref
- Jyun-Yu Jiang, Yen-Yu Ke, Pao-Yu Chien, and Pu-Jen Cheng. 2014. Learning user reformulation behavior for query auto-completion. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). ACM, New York, NY, 445--454. DOI:https://doi.org/10.1145/2600428.2609614Google ScholarDigital Library
- Eric R. Kandel, Yadin Dudai, and Mark R. Mayford. 2014. The molecular and systems biology of memory. Cell 157, 1 (2014), 163--186. DOI:https://doi.org/10.1016/j.cell.2014.03.001Google ScholarCross Ref
- Hyun-Kyu Kang and Key-Sun Choi. 1997. Two-level document ranking using mutual information in natural language information retrieval. Information Processing 8 Management 33, 3 (1997), 289--306.Google Scholar
- Gyuwan Kim. 2019. Subword language model for query auto-completion. In Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19).Google ScholarCross Ref
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). Association for Computational Linguistics, Doha, Qatar, 1746--1751. DOI:https://doi.org/10.3115/v1/D14-1181Google ScholarCross Ref
- Yann LeCun and Yoshua Bengio. 1998. The handbook of brain theory and neural networks. MIT Press, Cambridge, MA, Chapter Convolutional Networks for Images, Speech, and Time Series, 255--258.Google ScholarDigital Library
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436.Google Scholar
- Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, Hongyuan Zha, and Ricardo Baeza-Yates. 2015. Analyzing user’s sequential behavior in query auto-completion via markov processes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). ACM, New York, NY, 123--132. DOI:https://doi.org/10.1145/2766462.2767723Google ScholarDigital Library
- Tie-Yan Liu. 2009. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval 3, 3 (March 2009), 225--331.Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems—Volume 2 (NIPS’13). Curran Associates Inc., 3111--3119.Google ScholarDigital Library
- Dae Hoon Park and Rikio Chiba. 2017. A neural language model for query auto-completion. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’17). ACM, New York, NY, 1189--1192. DOI:https://doi.org/10.1145/3077136.3080758Google ScholarDigital Library
- Yang Song, Dengyong Zhou, and Li-wei He. 2011. Post-ranking query suggestion by diversifying search results. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’11). ACM, New York, NY, 815--824. DOI:https://doi.org/10.1145/2009916.2010025Google ScholarDigital Library
- Saedeh Tahery and Saeed Farzi. 2020. Customized query auto-completion and suggestion—A review. Information Systems 87 (2020), 101415. DOI:https://doi.org/10.1016/j.is.2019.101415Google ScholarCross Ref
- Po-Wei Wang, Huan Zhang, Vijai Mohan, Inderjit S. Dhillon, and J. Zico Kolter. 2018. Realtime query completion via deep language models. In the SIGIR 2018 Workshop on eCommerce co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), Ann Arbor, Michigan, July 12, 2018. http://ceur-ws.org/Vol-2319/paper24.pdf.Google Scholar
- Stewart Whiting, Andrew James McMinn, and Joemon M. Jose. 2013. Exploring real-time temporal query auto-completion. In DIR (DIR’13). 12--15.Google Scholar
- Aston Zhang, Amit Goyal, Weize Kong, Hongbo Deng, Anlei Dong, Yi Chang, Carl A. Gunter, and Jiawei Han. 2015. adaQAC: Adaptive query auto-completion via implicit negative feedback. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). ACM, New York, NY, 143--152. DOI:https://doi.org/10.1145/2766462.2767697Google ScholarDigital Library
- Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems (NIPS’15). 649--657.Google Scholar
- Zhaohui Zheng, Keke Chen, Gordon Sun, and Hongyuan Zha. 2007. A regression framework for learning ranking functions using relative relevance judgments. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’07). ACM, New York, NY, 287--294. DOI:https://doi.org/10.1145/1277741.1277792Google ScholarDigital Library
Index Terms
- Personalized Query Auto-Completion for Large-Scale POI Search at Baidu Maps
Recommendations
Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu Maps
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningPoint of interest auto-completion (POI-AC) is a featured function in the search engine of many Web mapping services. This function keeps suggesting a dynamic list of POIs as a user types each character, and it can dramatically save the effort of typing, ...
Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPoint Of Interest Auto-Completion (abbr. as POI-AC) is one of the featured functions for the search engine at Baidu Maps. It can dynamically suggest a list of POI candidates within milliseconds as a user enters each character (e.g., English, Chinese, or ...
Time-sensitive Personalized Query Auto-Completion
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementQuery auto-completion (QAC) is a prominent feature of modern search engines. It is aimed at saving user's time and enhancing the search experience. Current QAC models mostly rank matching QAC candidates according to their past popularity, i.e., ...
Comments