Research Article
Personalized Recommendation Algorithm Considering Time Sensitivity
@INPROCEEDINGS{10.1007/978-3-030-48513-9_12, author={Fuzhen Sun and Haiyan Zhuang and Jin Zhang and Zhen Wang and Kai Zheng}, title={Personalized Recommendation Algorithm Considering Time Sensitivity}, proceedings={Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019}, proceedings_a={CLOUDCOMP}, year={2020}, month={6}, keywords={Time sensitivity Stability of interest Prevalence of item Personalized recommendation Popularity bias}, doi={10.1007/978-3-030-48513-9_12} }- Fuzhen Sun
Haiyan Zhuang
Jin Zhang
Zhen Wang
Kai Zheng
Year: 2020
Personalized Recommendation Algorithm Considering Time Sensitivity
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-48513-9_12
Abstract
Aiming to solve the problem of goods popularity bias, this paper introduces the prevalence of items into user interest modeling, and proposes an item popularity model based on user interest feature. Usually, traditional model that does not take into account the stability of user’s interests, which leads to the difficulty in capturing their interest. To cope with this limitation, we propose a time-sensitive and stabilized interest similarity model that involves a process of calculating the similarity of user interest. Moreover, by combining those two kinds of similarity model based on weight factors, we develop a novel algorithm for calculation, which is named as IPSTS (IPSTS). To evaluate the proposed approach, experiments are performed and results indicate that Mean Absolute Difference (MAE) and root mean square error (RMSE) could be significantly reduced, when compared with those of traditional collaborative filtering Algorithms.