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CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services
Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items.
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i. e., in different geographical locations and at different times, user click behavior will change significantly.
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training
However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those non-robust but highly predictive features.
Context-aware Heterogeneous Graph Attention Network for User Behavior Prediction in Local Consumer Service Platform
Hence, in this paper, we propose a context-aware heterogeneous graph attention network (CHGAT) to dynamically generate the representation of the user and to estimate the probability for future behavior.
