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Collaborative filtering algorithm is currently the most widely used and a very efficient technology in personalized recommendation system. To overcome several defects in the research of the traditional Item-based collaborative filtering algorithm, this paper presents a optimized algorithm in two aspects, which are the selection of neighbors and the prediction of ratings. Firstly, different numbers...
With an ever-increasing amount of information made available via the Internet, it is getting more and more difficult to find the relevant pieces of information. Recommender systems have thus become an essential part of information technology. Although a lot of research has been devoted to this area, the factors influencing the quality of recommendations are not completely understood. This paper examines...
QoS prediction for Web services is a hot research problem in the field of services computing. As one of the most important methods for QoS prediction, Collaborative Filtering (CF) makes prediction based on the historical QoS data contributed by similar users and services. The key issue in this process is to detect the unreliable data offered by untrustworthy users, which has attracted limited attentions...
QoS prediction has become an important step in service recommending and selecting. Most QoS prediction approaches are using collaborative filtering as a prediction technique. But collaborative filtering may suffer from data sparsity problem which degrade the prediction accuracy. In order to alleviate the data sparsity problem of collaborative filtering, we presented a hybrid QoS prediction approach...
Existing service recommendation methods, that employ memory-based collaborative filtering (CF) techniques, compute the similarity between users or items using nonfunctional attribute values obtained at service invocation. However, using these nonfunctional attribute values from invoked services alone in similarity computation for personalized service recommendation is not sufficient. This is because...
Web service recommendation plays an important role in building reliable service-oriented systems for both the service providers and the active users. However, with the proliferation of web services on the World Wide Web, traditional service recommendation is hard to accurately provide customized services to active users. In this paper, we propose a novel web service recommender model using collaborative...
“Collaborative filtering” (CF) methods provide a good solution for recommendation systems. Neighborhood formation is considered as the main phase in memory approaches. Unfortunately, this phase encounters many problems such as sparsity and scalability, especially for huge datasets which consists of a large number of users and items. This paper presents a new hybrid approach for collaborative filtering...
Collaborative filtering method was widely used in the recommendation system. This method was able to provide recommendations to the user through the similarity values between users. However, the central issues in this method were new user issue and sparsity. This paper discusses about how to use matrix factorization and nearest-neighbour in film recommendation systems. Both of methods will be used...
Collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. As one of the most common approach to recommender systems, CF has been proved to be effective for solving the information overload problem. CF can be divided into two main branches: memory-based and model-based. Most of the present researches improve the accuracy of Memory-based algorithms...
This paper aims on collaborative filtering (CF) in TV recommendation system which combines content-based and collaborative filtering recommendation mechanism, we propose an algorithm that using the self-organizing mapping (SOM) to optimize the improved k-means (IK) clustering in collaborative filtering. The whole clustering algorithm is divided into two phases: at the first stage, the quantity of...
Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting...
Collaborative filtering (CF) is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it suffers from several inherent deficiencies such as data sparsity and cold start. To better show user preferences for the cold users additional information (e.g., trust) is often applied. We describe the stages based on which the ratings of an active...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivity concepts. By propagating "similarity" between users, in a similar way as with "trust", we can significantly expand the space of potential recommenders and system's coverage, improving also the recommendations' accuracy. While "trust" information might be missing or be...
Collaborative Filtering (CF) is one of the most effective technologies in making rating prediction for recommender systems. Traditional user-based CF methods take user-item matrices as input and compute the prediction value based on user similarity. The computation of similarity between each user pair requires exact matching on item set by finding a minimum number of same items rated by both users...
Client-side Quality-of-Service (QoS) evaluation of Web services is a critical factor in selecting the optimal Web service from a set of functionally equivalent service candidates. And collaborative filtering (CF) method becomes an important way for automatic QoS evaluation. Traditional CF approaches for this problem predict the QoS values by employing historical QoS information, but their performance...
Collaborative filtering technology is the mainstream recommendation technology in personalized recommendation system, the sparsity of the dataset plays a leading role in the prediction accuracy of the collaborative filtering algorithm. Virtual data filling and neighbors' calculation etc. are adopted to solve the sparsity problem in traditional methods, which lacked of dynamic changes of rating data...
The proliferation of powerful smart devices is revolutionizing mobile computing systems. A particular set of applications that is gaining wide interest is recommender systems. Recommender systems provide their users with recommendations on variety of personal and relevant items or activities. They can play a significant role in today's life whether in E-commerce or for daily decisions that we need...
In recommendation systems, the relationship between information size and recommendation performance is an important research point. Here, we study this relationship based on a new method, variable precision, and design a new algorithm. We demonstrate that recommendation systems perform better with higher data precision, however which should be controlled within a threshold. We collect movie rating...
QoS value prediction of Web services is an important research issue for service recommendation, selection and composition. Collaborative Filtering (CF) is one of the most widely used methods which employs QoS values contributed by similar users to make predictions. Therefore, historical QoS values contributed by different users can have great impacts on prediction results. However, existing Web service...
Recommendation system has been widely used in electronic commerce, news, web2.0, E-learning and other fields. Collaborative filtering is one of the most important algorithms. But as scale of recommendation system continues to expand, more and more problems appear. Data sparsity and poor prediction are main problems that recommendation system has to face. To improve the quality and performance, a new...
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