Relation-Level User Behavior Modeling for Click-Through Rate Prediction

Many recent user behavior based click-through rate models adopt a similar item-level paradigm: learn the user representation from a list of item representations via a sequence model and/or a pooling mechanism. However, sequence models are usually sensitive to the exact order of the behavior sequence, while item-level pooling mechanisms simply neglect the chronological information. In this paper, we balance the two approaches by decomposing the long item sequence into a group of extremely short sequences (item pairs) and conducting relational reasoning on them. More specifically, the relational reasoning mechanism consists of two parts, which are designed for capturing various transitional patterns in the behavior sequences. An attentive pooling layer is employed to emphasize those relation-level signals that are highly related to the target item. Therefore, our approach is essentially a middle ground between the previous two approaches. To verify the effectiveness of our method, we conduct extensive experiments on three public datasets. Experimental results show that our methods achieve better performance than others. Besides, we explore the properties of our model and verify the effectiveness of each component by controlled experiments.