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A Dual Channel Intent Evolution Network for Predicting Period-Aware Travel Intentions at Fliggy

Published: 17 October 2022 Publication History

Abstract

Fliggy of Alibaba group is one of the largest online travel platform (OTPs) in China, which provides travel products and travel experiences for tens of millions of online users by the personalized recommendation system (RS). User's future travel intent prediction is one key problem in travel scenario, which decides where and what to recommend, e.g., traveling to a surrounding city or a distant city. Such travel intent prediction problem has a lot of important applications, e.g., to push a notification with surrounding scenic spots recommendation to a user with intent to travel around, or to enable personalized promotion strategies to users with different intents. Existing studies on user's intent are largely sub-optimal for users' travel intent prediction at OTPs, since they rarely pay attentions to the characteristics of the travel industry, namely, user behavior sparsity due to low frequency of travel, spatial-temporal periodicity patterns, and the correlations between user's online and offline behaviors. In this paper, to address these challenges, we propose a dual channel intent evolution network based online-offline periodicity-aware network, DCIEN, for user's future travel intent prediction. In particular, it consists of two basic components including 1) Spatial-temporal Intent Patterns Network(ST-IPN), which exploits users' periodic intent patterns from offline data based on convolutional neural networks; 2) Periodicity-aware Intent Evolution Network(PA-IEN), which captures user's instant intent from online behaviors data and the interactions between online and offline intents. Extensive offline and online experiments on a real-world OTP demonstrate the superior performance of DCIEN over state-of-the-art methods.

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cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
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Published: 17 October 2022

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  1. neural networks
  2. travel intention prediction
  3. travel platforms

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  • (2024)Scalable Transformer for High Dimensional Multivariate Time Series ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679757(3515-3526)Online publication date: 21-Oct-2024
  • (2024)UID-Net: Enhancing Click-Through Rate Prediction in Trigger-Induced Recommendation Through User Interest DecompositionAdvanced Data Mining and Applications10.1007/978-981-96-0850-8_4(49-64)Online publication date: 24-Dec-2024

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