document: 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 .
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