  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.

Artificial Intelligence