finding group shilling in recommendation system
In the age of information explosion, recommendation system has been proved effective to cope with information overload in e-commerce area. However, unscrupulous producers shill the systems in many ways to make profit, and it makes the system imprecise and unreliable in a long term. Among many shilling behaviors, a new form of attack, called group shilling, appears and does great harm to the system. Because group shilling users are now well organized and become more hidden among various normal users, it is hard to find them by traditional methods. However, these group shilling users are similar to some extent, for they both shill the target items. We bring out a similarity spreading algorithm to find these group shilling users and protect recommendation system from unfair ratings. In our algorithm, we try to find these cunning group shilling users through propagating similarities from items to users iteratively. The experiment shows our similarity spreading algorithm improves the precision of the system and provides the system a reliable protection.
group group shilling recommendation system systems age informal effect information overload precise behavior attack users normalization method similarity algorithm protection experience reliability collaborative filtering