[1] |
李一野,邓浩江. 基于改进余弦相似度的协同过滤推荐算法[J]. 计算机与现代化, 2020(1):69-74.
|
[2] |
张世东. 推荐算法概述[J]. 科技传播, 2019,11(4):197-198.
|
[3] |
KIM T Y, LEE K S, AN Y E. A study on the recommendation of contents using speech emotion information and emotion collaborative filtering[J]. Journal of Digital Contents Society, 2018,19(12):2247-2256.
|
[4] |
RUI D, JIANG C Q, JAIN H K, et al. Integrating geographical and temporal influences into location recommendation:a method based on check-ins[J]. Information Technology and Management, 2019,20(2):73-90.
|
[5] |
姜书浩,张立毅,周娜. 基于用户偏好动态变化的协同过滤推荐[J]. 计算机与现代化, 2020(1):75-80.
|
[6] |
YANG B, LEI Y, LIU J M, et al. Social collaborative filtering by trust[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(8):1633-1647.
|
[7] |
SURYA K, TRIPTI M, VINAY K J, et al. LeaderRank based K-Means clustering initialization method for collaborative filtering[J]. Computers and Electrical Engineering, 2018,69:598-609.
|
[8] |
XU H P, JUN W, WEI J M. Recommending irregular regions using graph attentive networks[J]. Ad Hoc Networks, 2021,113:102383.
|
[9] |
刘健,张琨,陈旋. 基于标签和协同过滤的个性化推荐算法[J]. 计算机与现代化, 2016(2):62-65.
|
[10] |
CHEN H, LI Z K, HU W. An improved collaborative recommendation algorithm based on optimized user similarity[J]. The Journal of Supercomputing, 2016,72(7):2565-2578.
|
[11] |
WEI J, HE J H, CHEN K, et al. Collaborative filtering and deep learning based recommendation system for cold start items[J]. Expert Systems with Applications, 2016,69:29-39.
|
[12] |
WANG Y, WANG P Y, LIU Z, et al. A new item similarity based on α-divergence for collaborative filtering in sparse data[J]. Expert Systems with Applications, 2021,166:114074.
|
[13] |
蒋研. 基于协同过滤的个性化混合推荐算法及模型研究[D]. 南京:南京邮电大学, 2020.
|
[14] |
王全民,刘鑫,朱蓉,等. 一种新型的混合个性化推荐算法[J]. 计算机与现代化, 2013(8):64-67.
|
[15] |
唐泽坤,黄柄清,李廉. 基于改进Canopy聚类的协同过滤推荐算法[J]. 计算机应用研究, 2020,37(9):2615-2619.
|
[16] |
高娜,杨明. 嵌入LDA主题模型的协同过滤推荐算法[J]. 计算机科学, 2016,43(3):57-61.
|
[17] |
张利. 基于改进用户相似度的协同过滤推荐算法研究[J]. 现代计算机, 2019(17):34-38.
|
[18] |
蔡强,韩东梅,李海生,等. 基于标签和协同过滤的个性化资源推荐[J]. 计算机科学, 2014,41(1):69-71.
|
[19] |
何明,要凯升,杨芃,等. 基于标签信息特征相似性的协同过滤个性化推荐[J]. 计算机科学, 2018,45(S1):415-422.
|
[20] |
RASIWASIA N, VASCONCELOS N. Latent dirichlet allocation models for image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(11):2665-2679.
|
[21] |
TANG L, PENG S L, BI Y M, et al. A new method combining LDA and PLS for dimension reduction[J]. PLoS ONE, 2014,9(5):e96944.
|
[22] |
黄璐,林川杰,何军,等. 融合主题模型和协同过滤的多样化移动应用推荐[J]. 软件学报, 2017,28(3):708-720.
|
[23] |
GEORGE C, EDWARD I G. Explaining the gibbs sampler[J]. The American Statistician, 1992,46(3):167-174.
|
[24] |
黄贤英,龙姝言,谢晋. 结合用户兴趣度聚类的协同过滤推荐算法[J]. 计算机应用研究, 2019,36(9)2609-2612.
|
[25] |
郭彩云,王会进. 改进的基于标签的协同过滤算法[J]. 计算机工程与应用, 2016,52(8):56-61.
|
[26] |
李雯,文勇军,唐立军. 多特征融合的教育资源标签生成算法[J]. 计算机与现代化, 2020(9):19-24.
|