KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction

TL;DR

KAST is a novel model that adaptively segments user behavior sequences into sessions with similar intents, leveraging knowledge-aware modules to improve click-through rate prediction accuracy.

Contribution

The paper introduces KAST, a new approach that adaptively segments user sessions and incorporates structural knowledge, addressing issues of outliers and irregular time intervals in CTR prediction.

Findings

KAST outperforms state-of-the-art CTR prediction methods on public benchmarks.

The knowledge-aware module enhances session representation quality.

Adaptive session segmentation improves model robustness and accuracy.

Abstract

Capturing the evolving trends of user interest is important for both recommendation systems and advertising systems, and user behavior sequences have been successfully used in Click-Through-Rate(CTR) prediction problems. However, if the user interest is learned on the basis of item-level behaviors, the performance may be affected by the following two issues. Firstly, some casual outliers might be included in the behavior sequences as user behaviors are likely to be diverse. Secondly, the span of time intervals between user behaviors is random and irregular, for which a RNN-based module employed from NLP is not perfectly adaptive. To handle these two issues, we propose the Knowledge aware Adaptive Session multi-Topic network(KAST). It can adaptively segment user sessions from the whole user behavior sequence, and maintain similar intents in the same session. Furthermore, in order to improve the quality of session segmentation and representation, a knowledge-aware module is introduced so that the structural information from the user-item interaction can be extracted in an end-to-end manner, and a marginal based loss with these information is merged into the major loss. Through extensive experiments on public benchmarks, we demonstrate that KAST can achieve superior performance than state-of-the-art methods for CTR prediction, and key modules and hyper-parameters are also evaluated.