DeepAI AI Chat
Log In Sign Up

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

by   Hu Liu, et al.

Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest from rich behavior data. Existing user behavior modeling algorithms develop different attention mechanisms to emphasize query-relevant behaviors and suppress irrelevant ones. Despite being extensively studied, these attentions still suffer from two limitations. First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors. Second, these attentions are usually biased towards frequent behaviors, which is unreasonable since high frequency does not necessarily indicate great importance. To tackle the two limitations, we propose a novel attention mechanism, termed Kalman Filtering Attention (KFAtt), that considers the weighted pooling in attention as a maximum a posteriori (MAP) estimation. By incorporating a priori, KFAtt resorts to global statistics when few user behaviors are relevant. Moreover, a frequency capping mechanism is incorporated to correct the bias towards frequent behaviors. Offline experiments on both benchmark and a 10 billion scale real production dataset, together with an Online A/B test, show that KFAtt outperforms all compared state-of-the-arts. KFAtt has been deployed in the ranking system of a leading e commerce website, serving the main traffic of hundreds of millions of active users everyday.


page 1

page 2

page 3

page 4


Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

In the Click-Through Rate (CTR) prediction scenario, user's sequential b...

Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation

User interests are usually dynamic in the real world, which poses both t...

Category-Specific CNN for Visual-aware CTR Prediction at

As one of the largest B2C e-commerce platforms in China, also pow...

Dynamic Parameterized Network for CTR Prediction

Learning to capture feature relations effectively and efficiently is ess...

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

With the explosive growth of e-commerce, online transaction fraud has be...

Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling

User historical behaviors are proved useful for Click Through Rate (CTR)...

AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling

In Click-through rate (CTR) prediction models, a user's interest is usua...