TDTMF: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor. - Hao Ding 016; Qing Liu; Guangwei Hu (Inf. Process. Manag., 2022)

TNFRM: A recommendation model based on temporal interest fluctuation with neural networks and fuzzy clustering. - Hao Ding 016 (Appl. Intell., 2023)

GCNSLIM: Graph convolutional network with sparse linear methods for e-government service recommendation. - Lingyuan Kong; Hao Ding 016; Guangwei Hu (Knowl. Based Syst., 2024)