Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades because of its easy implementation and high recommendation accuracy. As the core of neighbourhood-based CF, the task of dynamically maintaining users’ similarity list is challenged by cold-start problem and scalability problem. Recently, several methods are presented on addressing the two problems. However, these methods require mn steps to compute the similarity list against the kNN attack, where m and n are the number of items and users in the system respectively. Observing that the k new users from the kNN attack, with enough recommendation data, have the same rating list, we present a faster algorithm, TwinSearch, to avoid computing and sorting the similarity list for each new user repeatedly to save the time. The computational cost of our algorithm is 1 125 of the existing methods. Both theoretical and experimental results show that the TwinSearch Algorithm achieves better running time than the traditional method.
@cite_9: Many commercial websites use recommender systems to help customers locate products and content. Modern recommenders are based on collaborative filtering: they use patterns learned from users' behavior to make recommendations, usually in the form of related-items lists. The scale and complexity of these systems, along with the fact that their outputs reveal only relationships between items (as opposed to information about users), may suggest that they pose no meaningful privacy risk. In this paper, we develop algorithms which take a moderate amount of auxiliary information about a customer and infer this customer's transactions from temporal changes in the public outputs of a recommender system. Our inference attacks are passive and can be carried out by any Internet user. We evaluate their feasibility using public data from popular websites Hunch, Last. fm, Library Thing, and Amazon.
Unfortunately, the current methods on cold-start problem and scalability problem do not work well on a special case: the new users, with enough recommendation data, have the same rating list ( @math Nearest Neighbour ( @math NN) attack @cite_9 can be taken as an example of our special case, which creates @math same fake users with at least 8 rated items into the recommender system). The reasons are the solutions to cold-start problem only work on the new users which have not been gathered sufficient information, and the methods concentrating on scalability problem only work on the old users who have already have a similarity list. Naturally, when facing the special case, the above methods have to apply the traditional similarity computation method which yields in @math time complexity. Considering the number of users in a recommender system, @math , is usually very large, the computational cost of the above method will be very large. Therefore, it is necessary to gain a faster algorithm to build the new users similarity list in our special case.