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基于用户声誉的鲁棒协同推荐算法

DOI: 10.16383/j.aas.2015.c140073, PP. 1004-1012

Keywords: 推荐系统,协同过滤,声誉,托攻击

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Abstract:

?随着推荐系统在电子商务界的快速发展以及取得的巨大经济收益,有目的性的托攻击是目前协同过滤系统面临的重大安全威胁,研究一种可抵御攻击的鲁棒推荐技术已成为目前推荐系统领域的重要课题.本文利用历史记录得到用户声誉,建立声誉推荐系统,并结合协同过滤推荐领域内的隐语义模型,提出基于用户声誉的隐语义模型鲁棒协同算法.本文提出的算法从人为攻击和自然噪声两个方面对系统的鲁棒性进行了改善.在真实的数据集Movielens1M上的实验表明,与现有的鲁棒性推荐算法相比,这种算法具有形式简单、可解释性强、稳定的特点,且在精度得到一定提升的情况下大大增强了系统抵御攻击的能力.

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