Robust collaborative filtering

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.

That is, malicious users or a competitor may deliberately inject certain number of fake profiles to the system (typically 1~5%) in such a way that they can affect the recommendation quality or even bias the predicted ratings on behalf of their advantages.

However, item-based collaborative filtering are still not completely immune to bandwagon and segment attacks.

Because shilling attacks inject not just single fake profile but a large number of similar fake profiles, these spam users will have unusually high similarity than normal users do.