AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query

TL;DR

AMinerGNN is a novel heterogeneous graph neural network model designed for paper recommendation that effectively fuses user and keyword queries, demonstrating superior performance through experiments and online tests.

Contribution

It introduces a new CTR prediction model that fuses user and keyword queries using a graph neural network with a query attentive fusion layer.

Findings

Outperforms existing methods in CTR prediction.

Effectively fuses two different queries for recommendation.

Proven superior through experiments and online A/B tests.

Abstract

Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword. This is a recommendation task with two queries, a.k.a. user ID and keyword. However, existing methods focus on recommendation according to one query, a.k.a. user ID, and are not applicable to solving this problem. In this paper, we propose a novel click-through rate (CTR) prediction model with heterogeneous graph neural network, called AMinerGNN, to recommend papers with two queries. Specifically, AMinerGNN constructs a heterogeneous graph to project user, paper, and keyword into the same embedding space by graph representation learning. To process two queries, a novel query attentive fusion layer is designed to recognize their importances dynamically and then fuse them as one query to build a unified and end-to-end recommender system. Experimental results on our proposed dataset and online A/B tests prove the superiority of AMinerGNN.