Abstract: For the problem that the traditional Collaborative Filtering（CF） algorithms appear lower precision after clustering，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 results show that the presented algorithm can improve the performance of CF systems in both the recommendation quality and efficiency.

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。