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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...
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...
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...
Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental...
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...
With the development of personalized recommendation, the method of user interest prediction has been a hot research topic. Usually, predict methods use individual related parameters such as user ratings to infer possible user interests. A potential problem with these methods is that the credibility of the user ratings is rarely questioned or considered during the process of prediction. However, as...
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...
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...
Collaborative filtering (CF) is a widely-used technique for generating personalized recommendations. CF systems are typically based on the ratings given by users to items. There are many factors influencing user's rating, beside user's interest and rating scale, item objective character is also the important element. Considering these factors, the improved item prediction approaches present a more...
In the field of collaborative filtering recommendation, the accuracy requirement of the recommendation algorithm always makes it complex and hard to realize. Slope One algorithm, as a recent proposed algorithm, was not only easy to achieve, but also efficient and effective. However, Slope One algorithm performs not so well when dealing with personalized recommendation tasks which concern the relationship...
With the dramatic increase of the amount of network information, the phenomenon of merchandise information overload has become more and more serious. Thus, the e-commerce sites are required to provide the most needed information for customers to attract their attention. The traditional recommendation algorithm ignores the connections among user's own attributes and the changes in project scoring with...
Collaborative filtering is one of widely used Web service recommendation techniques. There have been several methods of Web service selection and recommendation based on collaborative filtering, but seldom have they considered personalized influence of users and services. In this paper, we present an effective personalized collaborative filtering method for Web service recommendation. A key component...
This paper describes a new technique for making personalized recommendations. Among existing recommendation algorithms, user-based Collaborative Filtering (CF) approach is the most promising one. However, the problems like multiple-interests lead to a decline in recommendations' quality. To overcome the problem of multiple-interests and dimensionality curse as well, we propose a modified CF method...
Collaborative filtering is one of the most important technologies in e-commerce recommendation system. Traditional similarity measure methods work poorly when the user rating data are extremely sparse. Aiming at this issue a hybrid collaborative filtering is proposed. This method used a novel similarity measure method to predict the target item rating and it fused the advantages of the user-based...
With its unique advantages, the traditional collaborative filtering algorithm has been one of the original and most successful methods in product recommendation. However, the intensified sparsity of the matrix has caused a decreasing precision of particular recommendations, which the traditional CF algorithm is of little help. By making statistical analysis about users' selected items, this paper...
Collaborative filtering (CF) has been a comprehensive approach in recommendation system. But data are always sparse; any given user has seen or buys only a small fraction of all items. This becomes the bottleneck of CF. Cluster-based smoothing technique for nature language processing is successful to estimate probability of the unseen term by using the topic (cluster) of the term belongs to, which...
With the development of personalized recommendation systems, the research of collaborative filtering reached a bottleneck. Neither algorithm accuracy nor computational complexity can be improved significantly. In this paper, we present our statistics and analysis on some recognized datasets. The analysis shows that the real rating features of the users cannot follow even distribution while most current...
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