摘要：

提出一种基于记忆原理的推荐系统托攻击检测模型。利用短时记忆元和长时记忆元所描述的记忆增强和衰减规律，以及这2种记忆元与综合记忆元的联系，对托攻击进行检测。该模型的特征记忆库可及时更新，由此节省系统开销。实验结果证明，基于该模型的推荐系统具有较高的托攻击检测正确率。

关键词: 记忆原理, 推荐系统, 托攻击, 检测模型, 协同过滤

Abstract:

This paper proposes a shilling attack detection model for recommender system based on memory principle. By combining biological memory principle and mathematics statistics, it detects shilling attacks through the memory cell’s characteristics. The characteristic memory database can update timely, so that costs of system are saved. Experimental result shows that the model improves the ability of detecting shilling attacks of recommender system.

Key words: memory principle, recommender system, shilling attack, detection model, collaborative filtering

中图分类号: 

• TP391