基于最优权的协同过滤混合推荐算法及应用  

Collaborative Filtering Hybrid Recommendation AlgorithmBased on Optimal Weight and its Application

作　　者：于翘楚 赵明清 罗雨婷 YU Qiaochu;ZHAO Mingqing;LUO Yuting(College of Mathematics and Systems Science,Shandong University of Science and Technology,Qingdao 266590,China;College of Mechanical and Electronic Engineering,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]山东科技大学数学与系统科学学院,山东青岛266590 [2]山东科技大学机械电子工程学院,山东青岛266590

出　　处：《运筹与管理》2024年第7期79-84,共6页Operations Research and Management Science

基　　金：山东省自然科学基金面上项目(ZR2022MG061)。

摘　　要：针对传统协同过滤推荐算法预测精度不高、推荐质量低的问题,提出了一种基于最优组合预测思想的协同过滤混合推荐算法(BEST-CF),并利用基于用户的协同过滤推荐算法(User-CF)和基于项目的协同过滤推荐算法(Item-CF)的最优组合在Movielens 100K数据集上验证了BEST-CF的有效性,实验结果表明:BEST-CF算法明显提高了评分预测精度,能够提升推荐质量。最后,将BEST-CF用于保险产品的推荐,实验结果表明,BEST-CF的推荐准确度明显高于Item-CF和User-CF的,能为客户更加精准地推荐所偏好的保险产品。Collaborative filtering recommendation,as a relatively mature information filtering technology in the recommendation algorithm,is widely used in the field of commodity recommendation,but it faces problems such as data sparsity and cold start,which will reduce the recommendation quality of the algorithm.In view of the low prediction accuracy and low recommendation quality of traditional collaborative filtering recommendation algorithms,many scholars have proposed some improved algorithms to improve the accuracy of a single algorithm.Some scholars have improved the similarity measure to improve the effect of a single collaborative filtering recommendation algorithm,but they do not comprehensively use the recommendation information of multiple single recommendation algorithms to further improve the quality of recommendation,and hybrid recommendation algorithm is an effective strategy to solve this problem.Although the idea of mixed strategy alleviates the sparsity of data and improves the recommendation accuracy,the weight determination lacks theoretical basis and is too subjective.So this paper proposes a collaborative filtering hybrid recommendation algorithm(BEST-CF)based on the idea of optimal combination prediction.Collaborative filtering recommendation mainly uses the similarity between users or the similarity between items to predict the user’s rating of the item,its essence is the prediction problem.Different recommendation algorithms have different score prediction.In order to make effective use of different score prediction results,overcome the subjectivity existing in determining algorithm weight,and improve the quality of mixed recommendation more effectively,this paper applies the idea of optimal combination prediction to collaborative filtering mixed recommendation,so as to improve the accuracy of score prediction.The BEST-CF algorithm obtains the optimal weight by constructing the optimal combination prediction model,and uses the optimal combination of the user-based collaborative filtering recommen

关 键 词：协同过滤推荐 最优组合预测 算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]