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An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework

Published: 16 June 2023 Publication History

Abstract

Graph convolutional networks (GCNs) are taking over collaborative filtering-based recommendation. Their message-passing schema effectively distills the collaborative signal throughout the user-item graph by propagating informative content from neighbor to ego nodes. In this demonstration, we show how to run complete experimental pipelines with six state-of-the-art graph recommendation models in Elliot (i.e., our framework for recommender system evaluation). We seek to highlight four main features, namely: (i) we support reproducibility in PyTorch Geometric (i.e., the library we use to implement the baselines); (ii) reproduced graph models span across various GCN families; (iii) we prepare a Docker image to provide a self-consistent ecosystem for the running of experiments. Codes, datasets, and a video tutorial to install and launch the application are accessible at: https://github.com/sisinflab/Graph-Demo.

References

[1]
Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021. Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. In SIGIR. ACM, 2405–2414.
[2]
Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021. V-Elliot: Design, Evaluate and Tune Visual Recommender Systems. In RecSys. ACM, 768–771.
[3]
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, and Claudio Pomo. 2023. Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering. In ECIR (1)(Lecture Notes in Computer Science, Vol. 13980). Springer, 33–48.
[4]
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, and Claudio Pomo. 2022. How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering. In MORS@RecSys(CEUR Workshop Proceedings, Vol. 3268). CEUR-WS.org.
[5]
Chen Chen, Jie Guo, and Bin Song. 2021. Dual Attention Transfer in Session-based Recommendation with Multi-dimensional Integration. In SIGIR. ACM, 869–878.
[6]
Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, and Philip S. Yu. 2021. Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer. In CIKM. ACM, 433–442.
[7]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. ACM, 639–648.
[8]
Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In WWW. ACM, 2320–2329.
[9]
Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019. Disentangled Graph Convolutional Networks. In ICML(Proceedings of Machine Learning Research, Vol. 97). PMLR, 4212–4221.
[10]
Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. 2021. UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation. In CIKM. ACM, 1253–1262.
[11]
memory-efficient-aggregation-pytorch-geometric 2022. PyTorch Geometric Documentation. MEMORY-EFFICIENT AGGREGATIONS.https://pytorch-geometric.readthedocs.io/en/latest/notes/sparse_tensor.html. Accessed online on 15-05-2023.
[12]
pytorch-geometric-message-passing 2022. PyTorch Geometric Documentation. Creating Message Passing Networks.https://pytorch-geometric.readthedocs.io/en/latest/notes/create_gnn.html. Accessed online on 15-05-2023.
[13]
scatter-reproducibility-pytorch-geometric 2021. GitHub. PyG Team. PyTorch_Geometric. Issues. How to make scatter (Just CUDA) results repeatable.https://github.com/pyg-team/pytorch_geometric/issues/2788. Accessed online on 15-05-2023.
[14]
Yifei Shen, Yongji Wu, Yao Zhang, Caihua Shan, Jun Zhang, Khaled B. Letaief, and Dongsheng Li. 2021. How Powerful is Graph Convolution for Recommendation?. In CIKM. ACM, 1619–1629.
[15]
Jinbo Song, Chao Chang, Fei Sun, Xinbo Song, and Peng Jiang. 2020. NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation. CoRR abs/2010.12256 (2020).
[16]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In SIGIR. ACM, 165–174.
[17]
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In SIGIR. ACM, 1001–1010.
[18]
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. ACM, 726–735.
[19]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In KDD. ACM, 974–983.
[20]
Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. 2021. Socially-Aware Self-Supervised Tri-Training for Recommendation. In KDD. ACM, 2084–2092.
[21]
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation. In SIGIR. ACM, 1294–1303.
[22]
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, and Liang Wang. 2021. Mining Latent Structures for Multimedia Recommendation. In ACM Multimedia. ACM, 3872–3880.
[23]
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, and Ji-Rong Wen. 2022. RecBole 2.0: Towards a More Up-to-Date Recommendation Library. In CIKM. ACM, 4722–4726.
[24]
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, and Ji-Rong Wen. 2021. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. In CIKM. ACM, 4653–4664.
[25]
Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, and Rui Zhang. 2022. BARS: Towards Open Benchmarking for Recommender Systems. In SIGIR. ACM, 2912–2923.

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  • (2023)Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven AnalysisProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609489(350-361)Online publication date: 14-Sep-2023

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  1. An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework

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      cover image ACM Conferences
      UMAP '23 Adjunct: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
      June 2023
      446 pages
      ISBN:9781450398916
      DOI:10.1145/3563359
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 16 June 2023

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      1. Docker
      2. Graph Convolutional Networks
      3. Recommendation

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      • (2023)Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven AnalysisProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609489(350-361)Online publication date: 14-Sep-2023

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