Accepted Papers

Long Presentation Papers:

1. MARS-gym: A Gym Framework to Model, Train, and Evaluate Recommender Systems for Marketplaces
   Marlesson Santana, Luckeciano Melo, Fernando Camargo, Bruno Brandão, Anderson Soares, Renan Oliveir, and Sandor Caetano
2. Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering
   Zheda Mai, Ga Wu, Kai Luo, and Scott Sanner
3. Interactive Knowledge Graph Attention Network for Recommender Systems
   Li Yang, Shijia E, Shiyao Xu, and Yang Xiang
4. Enhancing Multi-factor Friend Recommendation in Location-based Social Networks
   Bassem Samir and Neamat El-Tazi
5. A Hierarchical Knowledge and Interest Propagation Network for Recommender Systems
   Qinghong Chen, Huobin Tan, Guangyan Lin, and Ze Wang
6. DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
   Yujia Zheng, Siyi Liu, Zekun Li, and Shu Wu
7. A Recommender Algorithm: Gradient Recurrent Neural Network Applied to Yang-baxter-like Equation
   Ying Liufu, Long Jin, Shuai Li, and Mei Liu
8. Revenue Maximization using Multitask Learning for Promotion Recommendation
   Venkataramana Kini and Ashwin Manjunatha
9. Hybrid GNN-SR: Combining Unsupervised and Supervised Graph Learning for Session-based Recommendation
   Kai Deng, Jiajin Huang, and Jin Qin
10. Scenario-aware and Mutual-based Approach for Multi-scenario Recommendation in E-Commerce
    Yuting Chen, Yanshi Wang, Yabo Ni, Anxiang Zeng, and Lanfen Lin

Short Presentation Papers:

11. μ-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems
    Tomas Sousa Pereira, Carlos Soares, and Tiago Cunha
12. Attentive-feature Transfer based on Mapping for Cross-domain Recommendation Algorithm
    Zhen Liu
13. Cross-session Aware Temporal Convolutional Network for Session Based Recommendation
    Rui Ye, Qing Zhang, and Hengliang Luo
14. Efficient Distributed MST based Clustering for Recommender System
    Ahmad Shahzad and Frans Coenen
15. GCMCSR:A New Graph Convolution Matrix Complete Method with Side-information Reconstruction
    Kun Niu, Yicong Yu, Xipeng Cao, and Chao Wang
16. Hybrid Learning with Teacher-student Knowledge Distillation for Recommenders
    Hangbin Zhang, Raymond Wong, and Victor Chu