基于权重调节和用户偏好的协同过滤算法  

Collaborative Filtering Algorithm Based on Weight Adjustment and User Preference

作　　者：董立岩[1] 修冠宇 马佳奇 DONG Liyan;XIU Guanyu;MA Jiaqi(College of Computer Science and Technology,Jilin University,Changchun 130012,China;School of International,Beijing University of Posts and Telecommunication,Beijing 100876,China)

机构地区：[1]吉林大学计算机科学与技术学院,长春130012 [2]北京邮电大学国际学院,北京100876

出　　处：《吉林大学学报（理学版）》2020年第3期599-604,共6页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:61272209).

摘　　要：针对传统相似度计算方法只利用用户的评分信息这一显性反馈行为进行推荐,导致推荐效果不理想的问题,提出一种新的相似度计算方法,通过引入权重调节机制及用户行为偏好等隐性反馈信息,提升推荐的准确度.首先,根据负采样的反用户频率,降低流行物品全局软件工程的影响程度,并使用共同评分行为的最小权重,调节因共同评分数过少而导致的推荐准确度偏差.其次,提出项目偏好词定义,根据项目偏好词矩阵计算出在项目特征上具有共同偏好的用户.最后,在MovieLens数据集上进行实验对比分析,实验结果表明,改进后的相似度计算有较优的MAE值,且有更高的推荐准确性.Aiming at the problem that the traditional similarity calculation method only used the user’s rating information as the explicit feedback behavior to recommend, which led to the unsatisfactory recommendation effect, we proposed a new similarity calculation method to improve the accuracy of the recommendation by introducing implicit feedback information, such as weight adjustment mechanism and user behavior preference. Firstly, according to the anti-user frequency of negative sampling, the influence degree of global software engineering of popular items was reduced, and the minimum weight of the common scoring behavior was used to adjust the recommended accuracy deviation caused by few common scoring. Secondly, we proposed the definition of project preference words, and calculated users with common preference in project characteristics according to the matrix of project preference words. Finally, the experimental comparison and analysis on the MovieLens data set. The experimental results show that the improved similarity calculation has better MAE value and higher recommendation accuracy.

关 键 词：权重调节 用户偏好 协同过滤 推荐准确度 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]