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基于记忆原理的推荐系统托攻击检测模型 被引量：1

Shilling Attack Detection Model for Recommender System Based on Memory Principle
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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。 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.
作者 黄光球 刘嘉飞
机构地区 西安建筑科技大学管理学院
出处 《计算机工程》 CAS CSCD 2012年第5期25-29,34,共6页 Computer Engineering
基金 陕西省科学技术研究发展计划基金资助项目(2011K06-08) 陕西省教育厅科技计划基金资助项目(09JK524 11JK0772)
关键词 记忆原理 推荐系统 托攻击 检测模型 协同过滤 memory principle recommender system shilling attack detection model collaborative filtering
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献11

  • 1Varian R.Recommender Systems[J].Communications of the ACM,1997,40(3):56-58.
  • 2Goldberg D,Nichols D,Oki B M,et al.Using Collaborative Filtering to Weave an Information Tapestry[J].Communications of the ACM,1992,35(12):61-70.
  • 3Metha B,Nejdl W.Unsupervised Strategies for Shilling Detection and Robust Collaborative Filtering[J].User Modeling and User-adapted Interaction,2009,19(1/2):65-97.
  • 4Chirita P A,Nejdl W,Zamfir C.Preventing Shilling Attacks in Online Recommender Systems[C] //Proc.of WIDM’05.New York,USA:[s.n.] ,2005.
  • 5李聪,骆志刚,石金龙.一种探测推荐系统托攻击的无监督算法[J].自动化学报,2011,37(2):160-167. 被引量：22
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二级参考文献23

  • 1Shepitsen A,Gemmell J,Mobasher B,et al.Personalized Recommendation in Collaborative Tagging Systems Using Hierarchical Clustering[C] //Proceedings of the 2nd ACM International Conference on Recommender Systems.Lausanne,Switzerland:[s.n.] ,2008.
  • 2Mobasher B.Recommender Systems[M].Bremen,Germany:BottcherIT Verlag,2007.
  • 3Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extension. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
  • 4Li Q, Kim B M. Constructing user profiles for collaborative recommender system. In: Proceedings of the 6th Asia Pacific Web Conference. Hangzhou, China: Springer, 2004. 100--110.
  • 5Herlocker J L, Konstan J A, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1999. 230-237.
  • 6Mobasher B, Burke R, Bhaumik R, Sandvig J J. Attacks and remedies in collaborative recommendation. IEEE InteI1igent Systems, 2007, 22(3): 56-63.
  • 7Burke R, Mobasher B, Zabicki R, Bhaumik R. Identifying attack models for secure recommendation. In: Proceedings of the Beyond Personalization Workshop on the International Conference on Intelligent User Interfaces. San Diego, USA: ACM Press, 2005. 347-361.
  • 8Lam S K, Riedl J. Shilling recommender systems for fun and profit. In: Proceedings of the 13th International Conference on World Wide Web. New York, USA: ACM, 2004. 393-402.
  • 9O'Mahony M, Hurley N J, Kushmerick N, Silvestre G. Collaborative recommendation: a robustness analysis. ACM Transactions on Internet Technology, 2004, 4(4): 344-377.
  • 10Burke R, Mobasher B, Williams C, Bhaumik R. Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2006. 542-547.

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计算机工程
2012年 第5期

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