Collaborative recommendation

TLDR

Collaborative recommendation is an effective personalized information access technique, yet theoretical analysis of its effectiveness conditions is limited, especially regarding its two robustness aspects—accuracy and stability. The study investigates the robustness of collaborative recommendation against noisy product ratings. The authors formalize accuracy using machine‑learning models, develop a framework to assess stability in a common collaborative filtering algorithm, and evaluate both on several real‑world datasets. The analysis shows that collaborative recommendation’s robustness has practical implications for enterprise marketing security and contributes to a comprehensive theoretical understanding of the method.

Abstract

Collaborative recommendation has emerged as an effective technique for personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To explore this issue, we analyse the <i>robustness</i> of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings. There are two aspects to robustness: recommendation <i>accuracy</i> and <i>stability</i>. We formalize recommendation accuracy in machine learning terms and develop theoretically justified models of accuracy. In addition, we present a framework to examine recommendation stability in the context of a widely-used collaborative filtering algorithm. For each case, we evaluate our analysis using several real-world data-sets. Our investigation is both practically relevant for enterprises wondering whether collaborative recommendation leaves their marketing operations open to attack, and theoretically interesting for the light it sheds on a comprehensive theory of collaborative recommendation.