User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app. Further, we design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths. In particular, we leverage contrastive learning to augment user behavior paths, provide behavior path self-activation to alleviate the effect of noise, and adopt a two-level matching mechanism to identify the most appropriate candidate. Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model. Moreover, our model has been deployed on the Meituan food delivery platform and has accumulated 1.6% improvement in CTR and 1.8% improvement in advertising revenue.
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
The Chinese University of Hongkong, Shenzhen（香港中文大学（深圳））；Alibaba Group（阿里巴巴集团）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Xi’an Jiaotong University（西安交通大学）
Path-Guided Flow Matching for Dataset Distillation
Department of Machine Learning, MBZUAI, Abu Dhabi, UAE（机器学习系，MBZUAI，阿布扎赫德，阿联酋）；Department of Computer Vision, MBZUAI, Abu Dhabi, UAE（计算机视觉系，MBZUAI，阿布扎赫德，阿联酋）
