信任调整因子对人工智能驱动的协作过滤推荐算法的影响

Edwin Ngwawe, E. Abade, Stephen Mburu
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引用次数: 0

摘要

对人工智能(AI)的信任是当代计算模式中的一个主要问题。研究表明,人工智能系统可能会超越人类,最终导致人类灭绝。因此,必须对这些系统的行为进行控制,以避免可能被坏人利用。推荐系统是人工智能产品的变种,它可以学习购物者过去的数据,预测购物者可能喜欢的商品。这有助于确定可推荐给活跃用户的商品。研究表明,传统的推荐系统允许使用不可信的数据,诱使不法商贩误导学习过程。这可能会欺骗买家。本研究在人工智能学习管道中引入了信任调整因素。我们进行了实验,测试信任增强协同过滤推荐算法与经典算法在鲁棒性上的差异。在受到各种形式的档案注入攻击时,我们测量了两套算法的预测偏移和命中率。我们发现,在稳健性方面,信任增强变体算法明显优于经典协同过滤推荐算法,在预测偏移方面最多可提高 52%,在命中率方面最多可提高 18%。经 t 检验证实,结果表明,在推荐系统中嵌入信任调整因子可提高其稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Trust Adjustment Factor on Artificial Intelligence Driven Collaborative Filtering Recommendation Algorithm
Trust on artificial intelligence (AI) is a major concern in the contemporary computing paradigms. Studies show that AI systems may outsmart humans, leading to an ultimate extinction of mankind. Therefore, the behavior of these systems must be controlled to avert potential use by bad actors. Recommender systems, which are variant of AI products, learn shoppers past data and predict items that shoppers may prefer. This helps in identifying items that may be recommended to the active user. Studies indicate that classical recommender systems allow untrustworthy data, tempting unscrupulous dealers to misdirect the learning process. This potentially defrauds buyers. This study introduces trust adjustment factor into the AI learning pipeline. We conducted experiments to test the difference in robustness of the trust-enhanced collaborative filtering recommendation algorithm against the classical counterpart. Prediction shift and hit ratios for the two sets of algorithms were measured when subjected to various forms of profile injection attacks. We found that the trust-enhanced variant of the algorithm significantly outperforms classical collaborative filtering recommendation in terms of robustness by up to 52% when measured by prediction shift and by up to 18% when measured by hit ratio. Confirmed by t-test, results suggest that embedding trust adjustment factor into recommender systems improves its robustness.
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