Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems

Author:

Severin Nikita1ORCID,Savchenko Andrey2ORCID,Kiselev Dmitrii3ORCID,Ivanova Maria4ORCID,Kireev Ivan5ORCID,Makarov Ilya6ORCID

Affiliation:

1. Faculty of Computer Science, HSE University, Russian Federation

2. Sber AI Lab, Russian Federation and Laboratory of Algorithms and Technologies for Network Analysis, HSE University, Russian Federation

3. Artificial Intelligence Research Institute (AIRI), Russian Federation and HSE University, Russian Federation

4. Skolkovo Institute of Science and Technology, Russian Federation and Sber AI Lab, and Artificial Intelligence Research Institute (AIRI), Russian Federation

5. Sber AI Lab, Russian Federation

6. Artificial Intelligence Research Institute (AIRI), Russian Federation and AI Center, National University of Science and Technology MISIS, Russian Federation

Publisher

ACM

Reference34 articles.

1. S. Bonner and et al.2018. Temporal graph offset reconstruction: Towards temporally robust graph representation learning . In International Conference on Big Data. IEEE , Seattle, WA, USA, 3737–3746. S. Bonner and et al.2018. Temporal graph offset reconstruction: Towards temporally robust graph representation learning. In International Conference on Big Data. IEEE, Seattle, WA, USA, 3737–3746.

2. H. Chen and et al.2021. Highly Liquid Temporal Interaction Graph Embeddings . In Proceedings of the Web Conference 2021 . ACM, New York, NY, USA, 1639–1648. H. Chen and et al.2021. Highly Liquid Temporal Interaction Graph Embeddings. In Proceedings of the Web Conference 2021. ACM, New York, NY, USA, 1639–1648.

3. Paulo Cortez and Alice Silva . 2008 . Using data mining to predict secondary school student performance . In Proceedings of 5th Annual Future Business Technology Conference. EUROSIS-ETI, Porto, 5–12 . Paulo Cortez and Alice Silva. 2008. Using data mining to predict secondary school student performance. In Proceedings of 5th Annual Future Business Technology Conference. EUROSIS-ETI, Porto, 5–12.

4. Z. Cui and etal2021. DyGCN: Dynamic Graph Embedding with Graph Convolutional Network. arxiv:2104.02962 [cs.LG] Z. Cui and et al.2021. DyGCN: Dynamic Graph Embedding with Graph Convolutional Network. arxiv:2104.02962 [cs.LG]

5. Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding

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