A Survey Paper on Recommender Systems <s> Privacy Risks <s> Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for 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. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems. <s> BIB001 </s> A Survey Paper on Recommender Systems <s> Privacy Risks <s> 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. <s> BIB002
In most systems, users need to register before they can enjoy personalized recommendation. The registration process often requires them to provide some personal informations like their names, birth dates, postal code and email. Combinations of those required fields (attributes) may be highly identifying (Quasi-identifier 10 ). Personal preferences like those expressed to many recommender systems may become quasi-identifier, especially if some users express unusual preferences (S. Lam, D. Frankowski, and J. Riedl, 2006). User's preferences could then be used to re-identify him in another system. For example, a company like Netflix could use the preferences some users saved in its system to find them on a competitor website. Since not every users want their information to be disclosed or misused, the recommender should then protect itself against exposition of users informations or misuse of those informations. Recommender systems are also confronted with other type of problems such as security's related problem. Since being recommended is often promise of good selling, recommender are often target of manipulation from producers or malicious users BIB002 . For example, a book writer may try to alter the recommendation so that his book get recommended. Recent research by Dellarocas and others have shown that even popular systems such as Amazon and eBay have (and are) being manipulated BIB001 . Shilling attacks are one of the most discussed method by which the prediction of a recommender can be bias.