Enhancing the robustness of recommender systems against spammers

The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user’s purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems.推荐算法的准确率与多样性始终是推荐系统领域的研究热点。一套优秀的推荐系统不仅需要具备较高的准确率与多样性，还应具备足够的抗恶意垃圾用户攻击鲁棒性。然而，现有学术文献中对推荐系统鲁棒性问题的关注相对较少。本文系统研究了不同类型垃圾用户攻击行为对各类推荐算法推荐结果的影响，并进一步通过在经典K近邻（KNN）算法中融入用户已购物品的内部相似度，提出了一种改进推荐算法。当在线商业系统中存在恶意垃圾用户攻击时，该改进算法可有效提升抗攻击鲁棒性，因此在推荐准确率与多样性方面均优于传统推荐算法。