GRAPH COLLABORATIVE GUIDED SESSION-BASED RECOMMENDATION

Xinyi Zeng, Wen Shi, Yang Bai, Li Jin, Xiaoyu Li, Zhi Guo

References

  1. [1] J. Shin, Y. Park, and D. Lee, Who will be smart home users? Ananalysis of adoption and diffusion of smart homes, TechnologicalForecasting and Social Change, 134, 2018, 246–253.
  2. [2] M. Chu, T. Zhai, F. Xie, Y. Li, and T. Tao, Margin-constrainedpid controller tuning method for systems with parameteruncertainty, International Journal of Robotics and Automation,40(2), 2025.
  3. [3] A. Raza, L. Jingzhao, Y. Ghadi, M. Adnan, and M. Ali, Smarthome energy management systems: Research challenges andsurvey, Alexandria Engineering Journal, 92, 2024, 117–170.
  4. [4] J. Zhao, X. Luo, and Y. Li, A novel robot path planningalgorithm based on the improved wild horse optimiser withhybrid strategies, International Journal of Robotics andAutomation, 39(6), 2024, 515–533.
  5. [5] C. Gu, Q. Feng, C. Lu, C. Gu, X. Wu, and K. Wu,Towards automated robot manipulation: a unified active visionframework, International Journal of Robotics and Automation,38(4), 2023.
  6. [6] W. Hong, N.N. Zheng, L. Wu, Y. Ji, and Y. Weng, TheHierarchical Model for News Recommendation, MechatronicSystems and Control, 47(1), 2019.
  7. [7] H. Li, H. Li, S. Zhang, Z. Zhong, and J. Cheng, Intelligentlearning system based on personalized recommendationtechnology, Neural Computing and Applications, 31(9), 2019,4455–4462.
  8. [8] X. Hu, T. Jiang, and M. Wang, A hybrid recommendationmodel for online learning, International Journal of Roboticsand Automation, 37(5), 2022, 453–459.
  9. [9] Z. Li, C. Yang, Y. Chen, X. Wang, H. Chen, G. Xu, L. Yao, andM. Sheng, Graph and sequential neural networks in session-based recommendation: A survey, ACM Computing Surveys,57(2), 2025, 1–37.
  10. [10] . S. Wang, Q. Zhang, L. Hu, X. Zhang, Y. Wang,and C. Aggarwal, Sequential/session-based recommendations:Challenges, approaches, applications and opportunities, inProceedings of the 45th International ACM SIGIR Conferenceon Research and Development in Information Retrieval, 2022,pp. 3425–3428.
  11. [11] Q. Han, C. Zhang, R. Chen, R. Lai, H. Song, and L. Li,Multi-faceted global item relation learning for session-basedrecommendation, in Proceedings of the 45th InternationalACM SIGIR Conference on Research and Development inInformation Retrieval, 2022, pp. 1705–1715.
  12. [12] B. Hidasi, B. Hidasi, A. Karatzoglou, L. Baltrunas, and D.Tikk, Session-based recommendations with recurrent neuralnetworks, arXiv:1511.06939, 2015.380
  13. [13] T. Chen, and R.C.-W. Wong, Handling information lossof graph neural networks for session-based recommendation,in Proceedings of the 26th ACM SIGKDD InternationalConference on Knowledge Discovery Data Mining, 2020, 1172–1180.
  14. [14] F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P.Jiang, Bert4rec: Sequential recommendation with bidirectionalencoder representations from transformer, in Proceedings ofthe 28th ACM International Conference on Information andKnowledge Management, 2019, 1441–1450.
  15. [15] X. Zhang, H. Lin, B. Xu, C. Li, Y. Lin, H. Liu, andF. Ma, Dynamic intent-aware iterative denoising networkfor session-based recommendation, Information Processing &Management, 59(3), 2022, 102936.
  16. [16] W. Shalaby, S. Oh, A. Afsharinejad, S. Kumar, and X.Cui, M2trec: Metadata-aware multi-task transformer for large-scale and cold-start free session-based recommendations, inProceedings of the 16th ACM Conference on RecommenderSystems, 2022, 573–578.
  17. [17] R. Esmeli, H. Abdullahi, M. Bader-El-Den, A.S. Can, Sessioncontext data integration to address the cold start problem ine-commerce recommender systems, Decision Support Systems,187, 2024, 114339.
  18. [18] Z. Yuan, F. Yuan, Y. Song, Y. Li, J. Fu, F. Yang, Y. Pan,and Y. Ni, Where to go next for recommender systems? id-vs.modality-based recommender models revisited, in Proceedingsof the 46th International ACM SIGIR Conference on Researchand Development in Information Retrieval, 2023, pp. 2639–2649.
  19. [19] X. Pan, Y. Chen, C. Tian, Z. Lin, J. Wang, H. Hu, andW.X. Zhao, Multimodal meta-learning for cold-start sequentialrecommendation, in Proceedings of the 31st ACM InternationalConference on Information Knowledge Management, 2022,3421–3430.
  20. [20] X. Zhang, B. Xu, C. Li, Y. Zhou, L. Li, and H. Lin, Sideinformation-driven session-based recommendation: A survey,arXiv:2402.17129, 2024.
  21. [21] J. Liang, X. Zhao, M. Li, Z. Zhang, W. Wang, H. Liu, and Z.Liu, Mmmlp: Multi-modal multilayer perceptron for sequentialrecommendations, in Proceedings of the ACM Web Conference2023, 2023, 1109–1117.
  22. [22] S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme,Factorizing personalized markov chains for next-basket recom-mendation, Proceedings of the 19th International Conferenceon World Wide Web, 2010, 811–820.
  23. [23] S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan,Session-based recommendation with graph neural networks, inProceedings of the AAAI Conference on Artificial Intelligence,33(01), 2019, 346–353.
  24. [24] C. Xu, P. Zhao, Y. Liu, V.S. Sheng, J. Xu, F. Zhuang, J. Fang,and X. Zhou, Graph contextualized self-attention network forsession-based recommendation, IJCAI, 19, 2019, 3940–3946.
  25. [25] R. Qiu, J. Li, Z. Huang, and H. Yin, Rethinking the item orderin session-based recommendation with graph neural networks,Proceedings of the 28th ACM International Conference onInformation and Knowledge Management, 2019, 579–588.
  26. [26] L. Liu, L. Wang, and T. Lian, CaSe4SR: Using categorysequence graph to augment session-based recommendation,Knowledge-Based Systems, 212, 2021, 106558.
  27. [27] A. Nagrani, S. Yang, A. Arnab, A. Jansen, C. Schmid, and C.Sun, Attention bottlenecks for multimodal fusion, Advances inNeural Information Processing Systems, 34, 2021, 14200–14213.
  28. [28] T. Baltrusaitis, C. Ahuja, and L.P. Morency, Multimodalmachine learning: A survey and taxonomy, IEEE Transactionson Pattern Analysis and Machine Intelligence, 41(2), 2019,423–443.
  29. [29] X. Zhang, B. Xu, F. Ma, C. Li, L. Yang, and H. Lin, Beyond co-occurrence: multi-modal session-based recommendation, IEEETransactions on Knowledge and Data Engineering, 36(4), 2024,1450–1462.
  30. [30] X. Zhang, B. Xu, L. Yang, C. Li, F. Ma, H. Liu, and H. Lin, Pricedoes matter! modelling price and interest preferences in session-based recommendation, in Proceedings of the 45th InternationalACM SIGIR Conference on Research and Development inInformation Retrieval, 2022, 1684–1693.
  31. [31] Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S.Ma, Explicit factor models for explainable recommendationbased on phrase-level sentiment analysis, Proceedings of the37th International ACM SIGIR Conference on ResearchDevelopment in Information Retrieval, 2014, 83–92.
  32. [32] Y. Zhang, X. Wang, H. Chen, and W. Zhu, Adaptivedisentangled transformer for sequential recommendation,Proceedings of the 29th ACM SIGKDD Conference onKnowledge Discovery and Data Mining, 2023, 3434–3445.
  33. [33] A. Malek, and F. Giv, Proportional–integral–derivativecontroller for armed manipulator robots, International Journalof Robotics and Automation, 39(3), 2024.
  34. [34] C.-H. Lee, Y.-H. Lee, and C.-C. Teng, A novel robust pidcontroller design by fuzzy neural network, Asian Journal ofControl, 4(4), 2002, 433–438.
  35. [35] S. Paliwal, M. Sharma, and L. Vig, Ossr-pid: one-shot symbolrecognition in p&id sheets using path sampling and gcn, 2021International Joint Conference on Neural Networks (IJCNN),IEEE, 2021, 1–8.
  36. [36] R. Ma, B. Zhang, Y. Zhou, Z. Li, and F. Lei, PID controller-guided attention neural network learning for fast and effectivereal photographs denoising, IEEE transactions on neuralnetworks and learning systems, 33(7), 2022, 3010–3023.
  37. [37] R. Ma, S. Li, B. Zhang, and Z. Li, Towards fast and robustreal image denoising with attentive neural network and PIDcontroller, IEEE Transactions on Multimedia, 24, 2022, 2366–2377.
  38. [38] R. Ma, S. Li, B. Zhang, and H. Hu, Meta PID attention networkfor flexible and efficient real-world noisy image denoising, IEEEtransactions on Image Processing: A Publication of the IEEESignal Processing Society, 31, 2022, 2053–2066.
  39. [39] J. G¨unther, E. Reichensd¨orfer, P.M. Pilarski, and K.Diepold, Interpretable PID parameter tuning for controlengineering using general dynamic neural networks: Anextensive comparison, PloS one, 15(12), 2020, e0243320.
  40. [40] D. Lausch, V. Naumann, O. Breitenstein, J. Bauer, A. Graff,J. Bagdahn, and C. Hagendorf, Potential-Induced Degradation(PID): Introduction of a novel test approach and explanationof increased depletion region recombination, IEEE Journal ofPhotovoltaics, 4(3), 2014, 834–840.
  41. [41] G. Macgregor, B.S. Lancho-Barrantes, and D.R. Pennington,Measuring the concept of PID literacy: User perceptionsand understanding of PIDs in support of open scholarlyinfrastructure, Open Information Science, 7(1), 2023.
  42. [42] S. Ekinci, B. Hekimo˘glu, and D. Izci, Opposition based Henrygas solubility optimization as a novel algorithm for PIDcontrol of DC motor, Engineering Science and Technology, anInternational Journal, 24(2), 2021, 331–342.
  43. [43] L. Wang, PID control system design and automatic tuningusing MATLAB/Simulink, John Wiley Sons, 2020.
  44. [44] S. Ren, and B. Li, Guest editorial: Intelligent automationtechnology and applications to environment, InternationalJournal of Robotics and Automation, 37(1), 2022.

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