Computer Engineering, Volume. 51, Issue 8, 151(2025)
Fused Transition Relation Regularization for Sequential Recommendation
[4] [4] Wang S, Hu L, Wang Y, et al. Sequential recommender systems: challenges, progress and prospects[C]//IJCAI. 2019.
[5] [5] Qiu J, Dong Y, Ma H, et al. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec[C]//Proceedings of the eleventh ACM international conference on web search and data mining. 2018: 459-467.
[6] [6] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014: 701-710.
[7] [7] Grover A, Leskovec J. node2vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016: 855-864.
[8] [8] Wu C Y, Ahmed A, Beutel A, et al. Recurrent recommender networks[C]//Proceedings of the tenth ACM international conference on web search and data mining. 2017: 495-503.
[9] [9] Zhu Y, Li H, Liao Y, et al. What to Do Next: Modeling User Behaviors by Time-LSTM[C]//IJCAI. 2017, 17: 3602-3608.
[10] [10] Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized markov chains for next-basket recommendation[C]//Proceedings of the 19th international conference on World wide web. 2010: 811-820.
[11] [11] Kang W C, McAuley J. Self-attentive sequential recommendation[C]//2018 IEEE international conference on data mining (ICDM). IEEE, 2018: 197-206.
[12] [12] Fan Z, Liu Z, Wang S, et al. Modeling sequences as distributions with uncertainty for sequential recommendation[C]//Proceedings of the 30th ACM international conference on information & knowledge management. 2021: 3019-3023.
[13] [13] Cai R, Wu J, San A, et al. Category-aware collaborative sequential recommendation[C]//Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021: 388-397.
[14] [14] Ding Y, Ma Y, Wong W K, et al. Modeling instant user intent and content-level transition for sequential fashion recommendation[J]. IEEE transactions on multimedia, 2021, 24: 2687-2700.
[15] [15] Trivedi R, Dai H, Wang Y, et al. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs[C]//international conference on machine learning. PMLR, 2017: 3462-3471.
[16] [16] Zhu Y, Chen Z. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems[C]//Proceedings of The ACM Web Conference 2022.2022: 2379-2387.
[17] [17] Shchur O, Bilo M, Gnnemann S. Intensity-free learning of temporal point processes[C]//Proceedings of the 8th International Conference on Learning Representations. 2020.
[18] [18] Chang X, Liu X, Wen J, et al. Continuous-time dynamic graph learning via neural interaction processes[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020: 145-154.
[19] [19] Tian S, Xiong T, Shi L. Streaming dynamic graph neural networks for continuous-time temporal graph modeling[C]//2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021: 1361-1366.
[20] [20] Zhang Y, Xiong Y, Li D, et al. CoPE: modeling continuous propagation and evolution on interaction graph[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021: 2627-2636.
[21] [21] Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 191-198.
[22] [22] Ying R, He R, Chen K, et al. Graph convolutional neural networks for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018: 974-983.
[23] [23] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
[24] [24] Rendle S, Freudenthaler C, Gantner Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009: 452-461.
[25] [25] He X, Liao L, Zhang H, et al. Neural collaborative filtering[C]//Proceedings of the 26th international conference on world wide web. 2017: 173-182.
[26] [26] Tay Y, Anh Tuan L, Hui S C. Latent relational metric learning via memory-based attention for collaborative ranking[C]//Proceedings of the 2018 world wide web conference. 2018: 729-739.
[27] [27] Kabbur S, Ning X, Karypis G. Fism: factored item similarity models for top-n recommender systems[C]//Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013: 659-667.
[28] [28] Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model[C]//Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008: 426-434.
[29] [29] Chen J, Zhang H, He X, et al. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 2017: 335-344.
[30] [30] He X, He Z, Song J, et al. NAIS: Neural attentive item similarity model for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(12): 2354-2366.
[31] [31] Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets[C]//2008 Eighth IEEE international conference on data mining. IEEE, 2008: 263-272.
[32] [32] Sedhain S, Menon A K, Sanner S, et al. Autorec: Autoencoders meet collaborative filtering[C]//Proceedings of the 24th international conference on World Wide Web. 2015: 111-112.
[33] [33] Liang D, Altosaar J, Charlin L, et al. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 59-66.
[34] [34] He X, Deng K, Wang X, et al. Lightgcn: Simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 2020: 639-648.
[35] [35] Liu J, Li D, Gu H, et al. Parameter-free Dynamic Graph Embedding for Link Prediction[J]. Advances in Neural Information Processing Systems, 2022, 35: 27623-27635.
[36] [36] Liu S, Liu J, Gu H, et al. AutoSeqRec: Autoencoder for Efficient Sequential Recommendation[C]//Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023: 1493-1502.
[37] [37] Zhu T, Shi Y, Zhang Y, et al. Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation[C]//Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 2024: 1003-1011.
Get Citation
Copy Citation Text
FENG Yali, WEN Wen, HAO Zhifeng, CAI Ruichu. Fused Transition Relation Regularization for Sequential Recommendation[J]. Computer Engineering, 2025, 51(8): 151
Category:
Received: --
Accepted: Aug. 26, 2025
Published Online: Aug. 26, 2025
The Author Email: