摘要
针对传统协同过滤推荐算法进行聚类后出现的推荐精度下降问题,提出了一种利用独特型网络模型对基于用户聚类的协同过滤算法加以改进的新思路。通过引入人工免疫中动态调节抗体浓度使免疫网络保持稳定的原理来调整邻居用户的数目,以保证邻居用户的多样性达到提高精度的目的。实验结果表明,该算法相对于传统的基于聚类的协同过滤算法而言,在提高推荐速度的同时保证了推荐的精度。
For the problem that the traditional Collaborative Filtering（CF） algorithms appear lower precision after elustering,a novel algorithm is proposed which employs the idiotypic immune networks to improve the CF based on user clustering. With the mechanism of artificial immune network dynamically adjusting the consistency of antibodies as well as the neighbor numbers,the algorithm makes the immune network stable,which ensures the system＇s diversity,and also increases its accuracy. Simulation resuits show that the presented algorithm can improve the performance of CF systems in both the recommendation quality and efficiency.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第27期141-144,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.60603026)~~
关键词
协同过滤
聚类
独特型网络
推荐系统
Collaborative Filtering（CF）
clustering algorithm
idiotypic networks
recommendation system
