Robust collaborative filtering

<p>Robust collaborative filtering, or attack-resistant collaborative filtering, refers to algorithms or techniques that aim to make <a href="/facts/Collaborative_filtering/NhSFypjN">collaborative filtering</a> more robust against efforts of manipulation, while hopefully maintaining recommendation quality. In general, these efforts of manipulation usually refer to shilling attacks, also called profile injection attacks. Collaborative filtering predicts a user's rating to items by finding similar users and looking at their ratings, and because it is possible to create nearly indefinite copies of user profiles in an online system, collaborative filtering becomes vulnerable when multiple copies of fake profiles are introduced to the system. There are several different approaches suggested to improve robustness of both model-based and memory-based collaborative filtering. However, robust collaborative filtering techniques are still an active research field, and major applications of them are yet to come.
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Robust collaborative filtering open-in-new

Robust collaborative filtering