Research Article
Robust Recommendation Algorithm Based on User Suspicious Probability and Item Weight
@INPROCEEDINGS{10.4108/eai.17-6-2022.2322755, author={Haihong Gao and Li Liu and Wenguang Zheng}, title={Robust Recommendation Algorithm Based on User Suspicious Probability and Item Weight}, proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2022}, month={10}, keywords={shilling attacks user suspicious probability relevance vector machine robust recommendation}, doi={10.4108/eai.17-6-2022.2322755} }- Haihong Gao
Li Liu
Wenguang Zheng
Year: 2022
Robust Recommendation Algorithm Based on User Suspicious Probability and Item Weight
ICIDC
EAI
DOI: 10.4108/eai.17-6-2022.2322755
Abstract
With the extensive development of recommendation technology, the threat of shilling attacks faced by the existing collaborative recommendation algorithms is also increasing sharply. To face more and more complex shilling attacks, this paper constructs a robust recommendation algorithm that can resist shilling attacks from the perspective of recommendation algorithm. Existing robust recommendation algorithms usually improve robustness by sacrificing some recommendation accuracy and reduce the recommendation accuracy. To solve this problem, this paper proposes a robust recommendation algorithm based on user suspicious probability and item weight. Firstly, we establish the relevance vector machine classifier according to user profile features to evaluate user suspicious probability in the database. Secondly, we construct singular value decomposition algorithm based on Hebbian learning and matrix factorization algorithm by integrating user suspicion information. Finally, a dynamic weighting scheme is used in combination with the above algorithm and the collaborative filtering algorithm based on item weight, and the above algorithms are mixed according to a certain weight to obtain a robust collaborative filtering algorithm SRICF. By adjusting the weight ratio, advantages of each algorithm are brought into play, thereby improving recommendation accuracy and robustness of the algorithm.
