Intent-Aware Audience Targeting for Ride-Hailing Service - Xia, Yuan; Zhou, Jingbo; Cao, Jingjia; Li, Yanyan; Gao, Fei; Liu, Kun; Wu, Haishan; Xiong, Hui (Machine Learning and Knowledge Discovery in Databases, 2019)
As the market for ride-hailing service is increasing dramatically, an efficient audience targeting system (which aims to identify a group of recipients for a particular message) for ride-hailing services is demanding for marketing campaigns. In this paper, we describe the details of our deployed system for intent-aware audience targeting on Baidu Maps for ride-hailing services. The objective of the system is to predict user intent for requesting a ride and then send corresponding coupons to the user. For this purpose, we develop a hybrid model to combine the LSTM model and GBDT model together to handle sequential map query data and heterogeneous non-sequential data, which leads to a significant improvement in the performance of the intent prediction. We verify the effectiveness of our method over a large real-world dataset and conduct a large-scale online marketing campaign over Baidu Maps app. We present an in-depth analysis of the model’s performance and trade-offs. Both offline experiment and online marketing campaign evaluation show that our method has a consistently good performance in predicting user intent for a ride request and can significantly increase the click-through rate (CTR) of vehicle coupon targeting compared with baseline methods.