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
2031305127 172068613 1979350723 2403905660
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. In this paper, we study a cold-start problem in recommendation systems where we have completely new users entered the systems. There is not any interaction or feedback of the new users with the systems previoustly, thus no ratings are available. Trivial approaches are to select ramdom items or the most popular ones to recommend to the new users. However, these methods perform poorly in many case. In this research, we provide a new look of this cold-start problem in recommendation systems. In fact, we cast this cold-start problem as a contextual-bandit problem. No additional information on new users and new items is needed. We consider all the past ratings of previous users as contextual information to be integrated into the recommendation framework. To solve this type of the cold-start problems, we propose a new efficient method which is based on the LinUCB algorithm for contextual-bandit problems. The experiments were conducted on three different publicly-available data sets, namely Movielens, Netflix and Yahoo!Music. The new proposed methods were also compared with other state-of-the-art techniques. Experiments showed that our new method significantly improves upon all these methods. A major challenge in collaborative filtering based recommender systems is how to provide recommendations when rating data is sparse or entirely missing for a subset of users or items, commonly known as the cold-start problem. In recent years, there has been considerable interest in developing new solutions that address the cold-start problem. These solutions are mainly based on the idea of exploiting other sources of information to compensate for the lack of rating data. In this paper, we propose a novel algorithmic framework based on matrix factorization that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, the proposed algorithm decouples the following two aspects of the cold-start problem: (a) the completion of a rating sub-matrix, which is generated by excluding cold-start users and items from the original rating matrix; and (b) the transduction of knowledge from existing ratings to cold-start items users using side information. This crucial difference significantly boosts the performance when appropriate side information is incorporated. We provide theoretical guarantees on the estimation error of the proposed two-stage algorithm based on the richness of similarity information in capturing the rating data. To the best of our knowledge, this is the first algorithm that addresses the cold-start problem with provable guarantees. We also conduct thorough experiments on synthetic and real datasets that demonstrate the effectiveness of the proposed algorithm and highlights the usefulness of auxiliary information in dealing with both cold-start users and items. In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the development of social network, trust measure introduced as a new approach to overcome the CF problems. On the other hand, trust-aware recommender systems are techniques to make use of trust statements and user personal data in social networks to improve the accuracy of rating prediction for cold start users. In addition, clustering-based recommender systems are other kind of systems that to be efficient and scalable to large-scale data sets but these systems suffer from relatively low accuracy and especially coverage too. Therefore to address these problems, in this paper we proposed a multi-view clustering based on Euclidean distance by combination both similarity view and trust relationships that is including explicit and implicit trusts. In order to analyze the effectiveness of the proposed method we used the real-world FilmTrust dataset. The experimental results on this data sets show that our approach can effectively improve both the accuracy and especially coverage of recommendations as well as in the cold start problem.