Triangle Graph Interest Network for Click-through Rate Prediction

by   Wensen Jiang, et al.

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both public and industrial datasets clearly verify the effectiveness of our framework.


page 1

page 2

page 3

page 4


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

Click-through rate (CTR) prediction is a critical task for many industri...

Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction, crucial in applications like recomm...

Soft Retargeting Network for Click Through Rate Prediction

The study of user interest models has received a great deal of attention...

Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution

Click-through rate (CTR) prediction is an essential task in industrial a...

Dynamic Sequential Graph Learning for Click-Through Rate Prediction

Click-through rate prediction plays an important role in the field of re...

Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction, whose goal is to estimate the proba...

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

Capturing the evolving trends of user interest is important for both rec...

Please sign up or login with your details

Forgot password? Click here to reset