X Ma, H Lu, Z Gan - Proceedings of the 23rd ACM international …, 2014 - dl.acm.org
With the booming of online social networks, social trust has been used to cluster users in recommender systems. It has been proven to improve the recommendation accuracy when …
D Jia, F Zhang, S Liu - J. Softw., 2013 - jsoftware.us
Collaborative filtering is one of the widely used technologies in the e-commerce recommender systems. It can predict the interests of a user based on the rating information …
L Guo, J Liang, Y Zhu, Y Luo, L Sun… - Journal of Intelligent …, 2019 - Springer
With the development of personalized recommendations, information overload has been alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of …
V Faridani, M Jalali, MV Jahan - International Journal of Data Science and …, 2017 - Springer
Collaborative filtering (CF) is one of the most well-known and commonly used techniques to build recommender systems and generate recommendations. However, it suffers from …
Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we …
The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network …
L Guo, K Luan, L Sun, Y Luo, X Zheng - Engineering Applications of …, 2023 - Elsevier
Using trust relationships can improve the accuracy of recommendation systems; however, it is affected by data sparsity. Random walks can harvest the behavioral relationships between …
Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a …
CC Than, SY Han - J. Internet Serv. Inf. Secur., 2014 - isyou.info
Recommender systems using traditional collaborative filtering suffer from some significant weaknesses, such as data sparseness and scalability. In this study, we propose a method …