中圖分類號：TP393
文獻標識碼：A
DOI:10.19358/j.issn.2097-1788.2023.10.003
引用格式：宋雨倫,，李大中,，張絲雨，等.基于混合偏好分析的聯(lián)邦推薦框架［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理,，2023,，42（10）：16-22.

Mixed preference analysis federated recommendation framework

Abstract： Targeting to solve the overfitting and overspecialization problems existing in contentbased recommendation algorithms, this paper proposed a Mixed Preference Analysis Federated Recommendation Framework (MPAFRF), which includes three modules: preference analysis module, federated interest grouping module and recommendation module with mixed preference analysis. In preference analysis module, a method to calculate the preference score of same and different attributes of a resource is proposed, mainly to solve the overfitting problem. The proposed framework overcames the overspecialization problem by first choosing a group, then assigning a preference score for each resource considering a mixed preference. And it uses grid search algorithm to find the best hyperparameter considering individual and group preference. The experimental results show that the proposed recommendation framework is enhanced in both performance and interpretability. The performance of the algorithm on specific task demonstrates that compared with the random forest algorithm, the framework proposed in this paper has a significant improvement in various indicators, and improves the interpretability of the recommendation system.

Key words : recommendation algorithm; preference analysis; contentbased recommendation; federated learning