[1] Xie Chengyan, Dong Lu,. Graph-enhanced neural interactive collaborative filtering [J]. Journal of Southeast University (English Edition), 2022, 38 (2): 110-117. [doi:10.3969/j.issn.1003-7985.2022.02.002]

Copy

Graph-enhanced neural interactive collaborative filtering()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

38

Issue:

2022 2

Page:

110-117

Research Field:

Automation

Publishing date:

2022-06-20

Info

Title:

Graph-enhanced neural interactive collaborative filtering

Author(s):

Xie Chengyan¹;  Dong Lu²

¹School of Automation, Southeast University, Nanjing 210096, China
²School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China

Keywords:

interactive recommendation systems;  cold-start;  graph neural network;  deep reinforcement learning

PACS:

TP18

DOI:

10.3969/j.issn.1003-7985.2022.02.002

Abstract:

To improve the training efficiency and recommendation accuracy in cold-start interactive recommendation systems, a new graph structure called item similarity graph is proposed on the basis of real data from a public dataset. The proposed graph is built from collaborative interactions and a deep reinforcement learning-based graph-enhanced neural interactive collaborative filtering(GE-ICF)model. The GE-ICF framework is developed with a deep reinforcement learning framework and comprises an embedding propagation layer designed with graph neural networks. Extensive experiments are conducted to investigate the efficiency of the proposed graph structure and the superiority of the proposed GE-ICF framework. Results show that in cold-start interactive recommendation systems, the proposed item similarity graph performs well in data relationship modeling, with the training efficiency showing significant improvement. The proposed GE-ICF framework also demonstrates superiority in decision modeling, thereby increasing the recommendation accuracy remarkably.

References:

[1] Wu Y, DuBois C, Zheng A X, et al. Collaborative denoising auto-encoders for top-n recommender systems [C]// Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. Los Angeles, CA, USA, 2016: 153-162. DOI: 10.1145/2835776.2835837.
[2] Chen X, Xu H, Zhang Y, et al. Sequential recommendation with user memory networks [C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. Los Angeles, CA, USA, 2018: 108-116. DOI:10.1145/3159652.3159668.
[3] Zhao X, Xia L, Tang J, et al. Deep reinforcement learning for search, recommendation, and online advertising:A survey [J]. ACM SIGWEB newsletter, 2019: 1-15. DOI: 10.1145/3320496.3320500.
[4] Wang H, Wu Q, Wang H. Factorization bandits for interactive recommendation [C]// Thirty-first AAAI Conference on Artificial Intelligence. San Francisco, CA, USA, 2017: 2695-2702. DOI: 10.5555/3298483.3298627.
[5] Zhao X, Zhang W, Wang J. Interactive collaborative filtering [C]// Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. Los Angeles, CA, USA, 2013: 1411-1420. DOI: 10.1145/2505515.2505690.
[6] Wu Q, Wang H, Hong L, et al. Returning is believing: Optimizing long-term user engagement in recommender systems [C]// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore, 2017: 1927-1936. DOI: 10.1145/3132847.3133025.
[7] Zou L, Xia L, Du P, et al. Pseudo Dyna-Q: A reinforcement learning framework for interactive recommendation [C]// Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, TA, USA, 2020: 816-824. DOI: 10.1145/3336191.3371801.
[8] Chen H, Dai X, Cai H, et al. Large-scale interactive recommendation with tree-structured policy gradient [C]// Proceedings of the AAAI Conference on Artificial Intelligence. Honolulu, Hawaii, USA, 2019, 33(1): 3312-3320. DOI: 10.1609/aaai.v33i01.33013312.
[9] Zheng G, Zhang F, Zheng Z, et al. DRN: A deep reinforcement learning framework for news recommendation [C]// Proceedings of the 2018 World Wide Web Conference. Lyon, France, 2018: 167-176. DOI: 10.1145/3178876.3185994.
[10] Zou L, Xia L, Gu Y, et al. Neural interactive collaborative filtering [C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Xi’an, China, 2020: 749-758. DOI:10.1145/3397271.3401181.
[11] Wang X, He X, Wang M, et al. Neural graph collaborative filtering [C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Paris, France, 2019: 165-174. DOI: 10.1145/3331184.3331267.
[12] Ma C, Ma L, Zhang Y, et al. Memory augmented graph neural networks for sequential recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence, New York, USA, 2020, 34(4): 5045-5052. DOI: 10.1609/aaai.v34i04.5945.
[13] Wang H, Zhao M, Xie X, et al. Knowledge graph convolutional networks for recommender systems [C]// The World Wide Web Conference. Los Angeles, CA, USA, 2019: 3307-3313. DOI: 10.1145/3308558.3313417.
[14] Lei Y, Pei H, Yan H, et al. Reinforcement learning based recommendation with graph convolutional q-network [C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Xi’an, China, 2020: 1757-1760. DOI: 10.1145/3397271.3401237.
[15] Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning [J]. Nature, 2015, 518(7540): 529-533. DOI:10.1038/nature14236.

Memo

Memo:

Biographies: Xie Chengyan(1996—), female, graduate; Dong Lu(corresponding author), female, doctor, associate professor, ldong90@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 62173251), the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, the Fundamental Research Funds for the Central Universities.
Citation: Xie Chengyan, Dong Lu.Graph-enhanced neural interactive collaborative filtering[J].Journal of Southeast University(English Edition), 2022, 38(2):110-117.DOI:10.3969/j.issn.1003-7985.2022.02.002.

Common functions