Personalized complementary product recommendation
Complementary product recommendation aims at providing product suggestions that are
often bought together to serve a joint demand. Existing work mainly focuses on modeling …
often bought together to serve a joint demand. Existing work mainly focuses on modeling …
Joint edge-model sparse learning is provably efficient for graph neural networks
Due to the significant computational challenge of training large-scale graph neural networks
(GNNs), various sparse learning techniques have been exploited to reduce memory and …
(GNNs), various sparse learning techniques have been exploited to reduce memory and …
Enhanced multi-relationships integration graph convolutional network for inferring substitutable and complementary items
H Chen, J He, W Xu, T Feng, M Liu, T Song… - Proceedings of the …, 2023 - ojs.aaai.org
Understanding the relationships between items can improve the accuracy and
interpretability of recommender systems. Among these relationships, the substitute and …
interpretability of recommender systems. Among these relationships, the substitute and …
Heterogeneous information networks: the past, the present, and the future
In 2011, we proposed PathSim to systematically define and compute similarity between
nodes in a heterogeneous information network (HIN), where nodes and links are from …
nodes in a heterogeneous information network (HIN), where nodes and links are from …
Multi-task gnn for substitute identification
Substitute product recommendation is important to improve customer satisfaction on E-
commerce domain. E-commerce in nature provides rich sources of substitute relationships …
commerce domain. E-commerce in nature provides rich sources of substitute relationships …
[HTML][HTML] Multi-task learning on heterogeneous graph neural network for substitute recommendation
Substitute recommendation in e-commerce has attracted increasing attention in recent
years, to help improve customer experience. In this work, we propose a multi-task graph …
years, to help improve customer experience. In this work, we propose a multi-task graph …
[HTML][HTML] Transitivity-encoded graph attention networks for complementary item recommendations
In e-commerce recommender systems, providing product suggestions to customers that are
often bought together, which is called “complementary recommendation,” not only improves …
often bought together, which is called “complementary recommendation,” not only improves …
Redrec: Relation and Dynamic Aware Graph Convolutional Network for Sequential Recommendation
R Yao, W Xu, Z Liu, Y Wang, Z Li… - 2023 8th IEEE …, 2023 - ieeexplore.ieee.org
In the daily lives, people always buy complements together instead of substitutes. These
item relationships in the user-item interaction sequences can have impact on the target item …
item relationships in the user-item interaction sequences can have impact on the target item …
Towards Trustworthy Machine Learning on Graph Data
J Ma - 2022 - deepblue.lib.umich.edu
Machine learning has been applied to more and more socially-relevant scenarios that
influence our daily lives, ranging from social media and e-commerce to self-driving cars and …
influence our daily lives, ranging from social media and e-commerce to self-driving cars and …