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Chen Ma
Researcher at McGill University
Publications - 45
Citations - 960
Chen Ma is an academic researcher from McGill University. The author has contributed to research in topics: Recommender system & Computer science. The author has an hindex of 11, co-authored 31 publications receiving 444 citations. Previous affiliations of Chen Ma include Beijing Institute of Technology.
Papers
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Proceedings ArticleDOI
Hierarchical Gating Networks for Sequential Recommendation
Chen Ma,Peng Kang,Xue Liu +2 more
TL;DR: Wang et al. as discussed by the authors proposed a hierarchical gating network (HGN) to capture both the long-term and short-term user interests in sequential recommender systems.
Journal ArticleDOI
Memory Augmented Graph Neural Networks for Sequential Recommendation
TL;DR: A graph neural network is applied to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items to demonstrate the effectiveness of the model for the task of Top-K sequential recommendation.
Proceedings ArticleDOI
Neighbor Interaction Aware Graph Convolution Networks for Recommendation
TL;DR: A novel framework NIA-GCN is proposed, which can explicitly model the relational information between neighbor nodes and exploit the heterogeneous nature of the user-item bipartite graph, and generalize to a commercial App store recommendation scenario.
Proceedings ArticleDOI
Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence
TL;DR: Li et al. as mentioned in this paper proposed a novel autoencoder-based model to learn the complex user-POI relations, namely SAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD).
Posted Content
Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence
TL;DR: A novel autoencoder-based model to learn the complex user-POI relations, namely SAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD), which adaptively differentiates the user preference degrees in multiple aspects, by adopting a multi-dimensional attention mechanism.