DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation

no code implementations • 30 May 2021 • Liqi Yang, Linhan Luo, Lifeng Xin, Xiaofeng Zhang, Xinni Zhang

Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demand-aware item embedddings for the later recommendations.

Session-Based Recommendations

RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating

no code implementations • 25 Nov 2020 • Linhao Luo, Liqi Yang, Ju Xin, Yixiang Fang, Xiaofeng Zhang, Xiaofei Yang, Kai Chen, Zhiyuan Zhang, Kai Liu

In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.