E-commerce User Purchase Intention Prediction Based on Temporal Behavior Modeling

The paper deals with the problems of heterogeneous behavior fusion and long-sequence interest modeling, which naturally leads to the proposal of a temporal prediction model in the Taobao user behavior dataset context, which combines a multi-behavior aware attention mechanism with gated recurrent units (GRU), and uses separate behavior embeddings to encode browsing, favoring, adding to cart, and purchasing. The paper first describes how it dynamically focuses on relevant behavior segments using an attention mechanism and then naturally introduces multitask learning to learn better representations. On the basis of empirical analysis, it convincingly establishes that user behavior has a single peak at noon, a sharp spike on weekends, and that adding items to the cart is the most reliable purchase signal. The experiments also show that the proposed model outperforms the baseline in both F1 score (0.926) and recall (0.938). Ablation experiments clearly and convincingly showed that behavior type embedding is the fundamental building block of the model, and hence its omission seriously degrades performance. Therefore, the paper naturally and logically establishes the effectiveness of temporal modeling for purchase intention prediction, which has direct implications for e-commerce applications.