Abstract
In the recommendation system, the collaborative filtering algorithm is widely used. However, there are lots of problems which need to be solved in recommendation field, such as low precision, the long tail of items. In this paper, we design an algorithm called FSTS for solving the low precision and the long tail. We adopt stability variables and time-sensitive factors to solve the problem of user's interest drift, and improve the accuracy of prediction. Experiments show that, compared with Item-CF, the precision, the recall, the coverage and the popularity have been significantly improved by FSTS algorithm. At the same time, it can mine long tail items and alleviate the phenomenon of the long tail.
Keywords
Cite this article
@article{cai-5024,
author = {Shanchen Pang and Shihang Yu and Guiling Li and Sibo Qiao and Min Wang},
title = {Time-Sensitive Collaborative Filtering Algorithm with Feature Stability},
journal = {Computing and Informatics},
volume = {39},
number = {1-2},
pages = {141--155},
year = {2020},
issn = {1335-9150},
doi = {10.31577/cai_2020_1-2_141}
}
TY - JOUR AU - Shanchen Pang AU - Shihang Yu AU - Guiling Li AU - Sibo Qiao AU - Min Wang TI - Time-Sensitive Collaborative Filtering Algorithm with Feature Stability JO - Computing and Informatics VL - 39 IS - 1-2 SP - 141 EP - 155 PY - 2020 SN - 1335-9150 DO - 10.31577/cai_2020_1-2_141 UR - https://doi.org/10.31577/cai_2020_1-2_141 KW - Collaborative filtering KW - recommendation algorithm KW - long tail KW - time-sensitive ER -
Pang, S., Yu, S., Li, G., Qiao, S., & Wang, M. (2020). Time-Sensitive Collaborative Filtering Algorithm with Feature Stability. Computing and Informatics, 39(1-2), 141–155. https://doi.org/10.31577/cai_2020_1-2_141