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]
   416   417   418   419   420   421   422   423   424   425   426