A Fuzzy Set Based Approach for Rating Bias
Abstract
In recommender systems, the user uncertain preference results in unexpected ratings. This paper makes an initial attempt in integrating the influence of user uncertain degree into the matrix factorization framework. Specifically, a fuzzy set of like for each user is defined, and the membership function is utilized to measure the degree of an item belonging to the fuzzy set. Furthermore, to enhance the computational effect on sparse matrix, the uncertain preference is formulated as a side-information for fusion. Experimental results on three real-world datasets show that the proposed approach produces stable improvements compared with others.
Cite
Text
Li et al. "A Fuzzy Set Based Approach for Rating Bias." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019969Markdown
[Li et al. "A Fuzzy Set Based Approach for Rating Bias." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/li2019aaai-fuzzy/) doi:10.1609/AAAI.V33I01.33019969BibTeX
@inproceedings{li2019aaai-fuzzy,
title = {{A Fuzzy Set Based Approach for Rating Bias}},
author = {Li, Mingming and Dai, Jiao and Zhu, Fuqing and Zang, Liangjun and Hu, Songlin and Han, Jizhong},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9969-9970},
doi = {10.1609/AAAI.V33I01.33019969},
url = {https://mlanthology.org/aaai/2019/li2019aaai-fuzzy/}
}