Page 421 - 《软件学报》2024年第6期
P. 421
钱忠胜 等: 融合自适应周期与兴趣量因子的轻量级 GCN 推荐 2997
selection method. Journal of Intelligent Information Systems, 2018, 51(1): 183–205. [doi: 10.1007/s10844-017-0493-0]
[9] Tey FJ, Wu TY, Lin CL, Chen JL. Accuracy improvements for cold-start recommendation problem using indirect relations in social
networks. Journal of Big Data, 2021, 8(1): 98. [doi: 10.1186/s40537-021-00484-0]
[10] Wang J, Li SJ, Yang S, Jin H, Yu W. A new transfer learning model for cross-domain recommendation. Chinese Journal of Computers,
2017, 40(10): 2367–2380 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2017.02367]
[11] Xu CH, Ding AS, Zhao KD. A novel POI recommendation method based on trust relationship and spatial-temporal factors. Electronic
Commerce Research and Applications, 2021, 48: 101060. [doi: 10.1016/j.elerap.2021.101060]
[12] He XN, Zhang HW, Kan MY, Chua TS. Fast matrix factorization for online recommendation with implicit feedback. In: Proc. of the 39th
Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Pisa: ACM, 2016. 549–558. [doi: 10.1145/2911451.
2911489]
[13] Liu XB, Nie XS, Yin YL. Mutual linear regression based supervised discrete cross-modal hashing. Journal of Computer Research and
Development, 2020, 57(8): 1707–1714 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.2020.20200122]
[14] Zhao N, Long Z, Wang J, Zhao ZD. AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN
encoder. Knowledge-based Systems, 2023, 259: 110078. [doi: 10.1016/j.knosys.2022.110078]
[15] Zheng Y, Shi XM, Liu JX. Multi-path back-propagation method for neural network verification. Ruan Jian Xue Bao/Journal of Software,
2022, 33(7): 2464–2481 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6585.htm [doi: 10.13328/j.cnki.jos.006585]
[16] Yu F, Liu Q, Wu S, Wang L, Tan TN. A dynamic recurrent model for next basket recommendation. In: Proc. of the 39th Int’l ACM
SIGIR Conf. on Research and Development in Information Retrieval. Pisa: ACM, 2016. 729–732. [doi: 10.1145/2911451.2914683]
[17] Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online learning of social representations. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on
Knowledge Discovery and Data Mining. New York: ACM, 2014. 701–710. [doi: 10.1145/2623330.2623732]
[18] Wu L, Li JW, Sun PJ, Hong RC, Ge Y, Wang M. DiffNet++: A neural influence and interest diffusion network for social
recommendation. IEEE Trans. on Knowledge and Data Engineering, 2022, 34(10): 4753–4766. [doi: 10.1109/TKDE.2020.3048414]
[19] Wu F, Zhang TY, de Souza Jr AH, Fifty C, Yu T, Weinberger KQ. Simplifying graph convolutional networks. In: Proc. of the 36th Int’l
Conf. on Machine Learning. Long Beach: PMLR, 2019. 6861–6871.
[20] Yu JL, Yin HZ, Li JD, Wang QY, Hung NQV, Zhang XL. Self-supervised multi-channel hypergraph convolutional network for social
recommendation. In: Proc. of the 2021 Web Conf. Ljubljana: ACM, 2021. 413–424. [doi: 10.1145/3442381.3449844]
[21] Yu JL, Yin HZ, Gao M, Xia X, Zhang XL, Hung NQV. Socially-aware self-supervised tri-training for recommendation. In: Proc. of the
27th ACM SIGKDD Conf. on Knowledge Discovery & Data Mining. Singapore: ACM, 2021. 2084 –2092. [doi: 10.1145/3447548.
3467340]
[22] Liang YW. Design and implementation of English picture book reading recommendation system based on forgetting curve [MS. Thesis].
Beijing: Beijing Jiaotong University, 2019 (in Chinese with English abstract). [doi: 10.26944/d.cnki.gbfju.2019.000095]
[23] Shi LH, Kou Y, Shen DR, Nie TZ, Li D. Recommendation method based on multi-view embedding fusion for HINs. Ruan Jian Xue
Bao/Journal of Software, 2022, 33(10): 3619–3634 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6632.htm [doi:
10.13328/j.cnki.jos.006632]
[24] Liu LL, Tan ZY, Shu J. Node importance estimation method for opportunistic network based on graph neural networks. Journal of
Computer Research and Development, 2022, 59(4): 834–851 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.2020
0673]
[25] Peng L, Hu RY, Kong F, Gan JZ, Mo YJ, Shi XS, Zhu XF. Reverse graph learning for graph neural network. IEEE Trans. on Neural
Networks and Learning Systems, 2022. [doi: 10.1109/TNNLS.2022.3161030]
[26] Wu L, Sun PJ, Fu YJ, Hong RC, Wang XT, Wang M. A neural influence diffusion model for social recommendation. In: Proc. of the
42nd Int ’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Pairs: ACM, 2019. 235 –244. [doi: 10.1145/
3331184.3331214]
[27] Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proc. of the
25th Int’l Conf. on Uncertainty in Artificial Intelligence. Montreal: AUAI Press, 2009. 452–461.
[28] Ji SY, Feng YF, Ji RR, Zhao XB, Tang WW, Gao Y. Dual channel hypergraph collaborative filtering. In: Proc. of the 26th ACM
SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining. San Diego: ACM, 2020. 2020–2029. [doi: 10.1145/3394486.3403253]
[29] Guo HF, Tang RM, Ye YM, Li ZG, He XQ. DeepFM: A factorization-machine based neural network for CTR prediction.
arXiv:1703.04247, 2017.
[30] Chen JW, Wang C, Zhou S, Shi QH, Chen JB, Feng Y, Chen C. Fast adaptively weighted matrix factorization for recommendation with
implicit feedback. Proc. of the AAAI Conf. on Artificial Intelligence, 2020, 34(4): 3470–3477. [doi: 10.1609/aaai.v34i04.5751]