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首页> 外文会议>2016 5th International Conference on Informatics, Electronics and Vision >Stable and reliable predictive accuracy of robust weighted slope one under profile injection attacks
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Stable and reliable predictive accuracy of robust weighted slope one under profile injection attacks

机译：稳固加权斜率一在剖面注入攻击下的稳定可靠预测精度

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摘要

The Slope One class of algorithms have been shown to lead, although being relatively simple, to accuracies that are very close to the more commonly utilised memory-based Collaborative Filtering (CF) algorithms. In addition, this class of algorithms is highly scalable in comparison to memory-based algorithms. A recently observed phenomenon are profile injection attacks on recommendation algorithms that tend to increase (push attack) or decrease (nuke attack) the recommendations of an item depending on the intentions of the attacker. Model-based algorithms have performed well under such attacks and are therefore preferred over memory-based CF algorithms despite their lower accuracy. Previous work showed that the recommendation ranking of items using the Robust Weighted Slope One (RWSO) algorithm is fairly stable under profile injection attacks compared to the Weighted Slope One (WSO) and the Improved Slope One (I-SO) approaches. In this paper, it is shown that, under profile injection attacks, the predictive accuracy of RWSO is more stable and hence more reliable than the predictive accuracy of WSO and I-SO.
机译：尽管相对简单，但已经显示出Slope One类算法可导致非常接近更常用的基于内存的协作过滤（CF）算法的精度。此外，与基于内存的算法相比，此类算法具有很高的可扩展性。最近观察到的现象是对推荐算法的配置文件注入攻击，这些攻击倾向于根据攻击者的意图增加（推式攻击）或减少（核攻击）项目的推荐。基于模型的算法在这种攻击下表现良好，因此尽管其准确性较低，但优于基于内存的CF算法。先前的工作表明，与“加权坡度一”（WSO）和“改进坡度一”（I-SO）方法相比，使用“稳健加权坡度一”（RWSO）算法对项目的推荐等级在配置文件注入攻击下相当稳定。本文表明，在轮廓注入攻击下，RWSO的预测精度比WSO和I-SO的预测精度更稳定，因此更可靠。

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