MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

TL;DR

The paper introduces MTBRN, a novel network leveraging multiplex relations like knowledge and similarity graphs to better understand user interests and improve CTR prediction accuracy.

Contribution

It proposes a new framework that models multiplex relations between user behaviors and target items using multiple graphs and path-based encoding.

Findings

Significant improvement in CTR prediction accuracy.

Effective modeling of multiplex relations enhances user interest understanding.

Validated through extensive offline and online experiments.

Abstract

Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences attracts much attention and shows great improvements in the CTR field. Existing works mainly exploit attention mechanism based on embedding product when considering relations between user behaviors and target item. However, this methodology lacks of concrete semantics and overlooks the underlying reasons driving a user to click on a target item. In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction. Multiplex relations consist of meaningful semantics, which can bring a better understanding on users' interests from different perspectives. To explore and model multiplex relations, we propose to incorporate various graphs (e.g., knowledge graph and item-item similarity graph) to construct multiple relational paths between user behaviors and target item. Then Bi-LSTM is applied to encode each path in the path extractor layer. A path fusion network and a path activation network are devised to adaptively aggregate and finally learn the representation of all paths for CTR prediction. Extensive offline and online experiments clearly verify the effectiveness of our framework.