Abstract
Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as 'shilling attacks' are performed to push an item or nuke an item in the system. This paper proposes an algorithm to detect such shilling profiles in the system accurately and also study the effects of such profiles on the recommendations.
Abstract (translated)
考虑到产品数量以指数方式增长，用户在做出决定之前可以吸收的数据量相对较小，因此推荐系统有助于根据用户喜好对内容进行分类。协同过滤是一种广泛使用的计算推荐的方法，因为它性能良好。但是，这种方法使得系统容易受到试图影响推荐结果的攻击，这些攻击被称为“推销攻击”。这些攻击旨在推动系统中的某个项目或彻底破坏它。本文提出了一种准确检测系统中“推销实例”的算法，并研究了这些实例对推荐的影响。
URL
https://arxiv.org/abs/2404.16177
