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.
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.
@cite_9
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The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommendations received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system's collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neural learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave-one-out cross validation.