Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items.
In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting recommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.
Read the entire article at:
http://www.l3s.de/web/upload/documents/chirita05preventing.pdf
Thursday, March 27, 2008
Thursday, March 20, 2008
Shilling Recommender Systems For Fun And Profit
Recommender systems have become particularly common in e-commerce, where recommendation of items can often help a customer find what he/she is interested in and, therefore can help drive sales.
Some users in order to increase their market penetration may find it profitable to "Shill" recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors.
This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions,how detectable the attacks are by the operator of the system,
and what the properties are of the items being attacked.
The questions are explored experimentally on a large data set of movie ratings. The results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.
Read the entire article at:
http://www.grouplens.org/papers/pdf/p333-lam.pdf
Some users in order to increase their market penetration may find it profitable to "Shill" recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors.
This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions,how detectable the attacks are by the operator of the system,
and what the properties are of the items being attacked.
The questions are explored experimentally on a large data set of movie ratings. The results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.
Read the entire article at:
http://www.grouplens.org/papers/pdf/p333-lam.pdf
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