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Leveraging user itinerary to improve personalized deep matching at Fliggy

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Abstract

Matching items for a user from a travel item pool of large cardinality has been the most important technology for Fliggy, one of the most popular online travel platforms (OTPs) in China. In this paper, we propose a novel Fliggy ITinerary-aware deep matching network (FitNET) to address the major challenges facing OTPs. FitNET is designed based on the effective deep matching framework. First, the concept of user active itinerary is well defined for OTPs. Then, several itinerary-aware attention mechanisms that capture the interactions between user active itineraries and other inputs are designed, to better infer users’ travel intentions, preferences, and handle their diverse needs. Then, two learning objectives, i.e., user travel intention prediction and user click behavior prediction, are proposed to be optimized simultaneously. In addition to the FitNET model, its improved version, named FitNET\(^+\), is also proposed. FitNET\(^+\) optimizes FitNET by additionally considering the information of a user’s historical itineraries and devising an effective itinerary weighting unit to control the impact of each historical itinerary on the learning of the user’s preferences. An offline experiment on the Fliggy production dataset and an online A/B test both show that FitNET and FitNET\(^+\) outperform other state-of-the-art methods, due to the idea that a user should be learned based on the granularity of his or her itinerary rather than on a single order. In addition, FitNET\(^+\) further improves FitNET by on average \(9.4\%\) in precision and \(2.4\%\) in hit rate, which indicates the importance of leveraging the historical itineraries of users to better capture their needs.

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Notes

  1. https://www.fliggy.com/

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Authors and Affiliations

  1. Guangxi University, Nanning, 110004, China

    Jia Xu

  2. Alibaba Group, Hangzhou, 311100, China

    Zulong Chen, Wanjie Tao, Ziyi Wang, Detao Lv & Yao Yu

  3. Concordia University, Montreal, Quebec, H3G 1M8, Canada

    Chuanfei Xu

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  1. Jia Xu
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  2. Zulong Chen
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  3. Wanjie Tao
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  4. Ziyi Wang
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  6. Yao Yu
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Correspondence to Zulong Chen or Wanjie Tao.

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This work is supported by the National Natural Science Foundation of China (No. 62067001) and the Special funds for Guangxi BaGui Scholars. This work is partially supported by the Guangxi Natural Science Foundation (No. 2019JJA170045).

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Xu, J., Chen, Z., Tao, W. et al. Leveraging user itinerary to improve personalized deep matching at Fliggy. The VLDB Journal 32, 1065–1086 (2023). https://doi.org/10.1007/s00778-023-00787-z

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  • DOI: https://doi.org/10.1007/s00778-023-00787-z

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