Collaborative filtering is amongst the most preferred techniques when implementing recommender systems. Recently, great interest has turned towards parallel and distributed implementations of collaborative filtering algorithms. This work is a survey of the parallel and distributed collaborative filtering implementations, aiming not only to provide a comprehensive presentation of the field's development, but also to offer future research orientation by highlighting the issues that need to be further developed.
One of the early surveys addressing recommender systems is @cite_21 . Recommender systems are classified into three categories. Content-based, collaborative and hybrid implementations. The constraints of each category are discussed and possible ways to improve the recommendation methods are proposed.
@cite_21
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Recommender systems have become an important research area because they have been a kind of Web intelligence techniques to search through the enormous volume of information available on the Internet. Collaborative filtering and content-based methods are two most commonly used approaches in most recommender systems. Although each of them has both advantages and disadvantages in providing high quality recommendations, a hybrid recommendation mechanism incorporating components from both of the methods would yield satisfactory results in many situations. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data and item data, and then provides personalized suggestions through user-based collaborative filtering and item-based collaborative filtering. The proposed system clusters on content-based approach and collaborative approach then it contribute to the improvement of prediction quality of a hybrid recommender system. This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmarm's (1999) aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. However, global probabilistic models coupled with standard EM learning algorithms tend to drastically overfit in the sparsedata situations typical of recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the Researchlndex library of Computer Science publications show that appropriate mixture models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN). Global probabilistic models also allow more general inferences than local methods like k-NN. Recommender systems are very useful due to the huge volume of information available on the Web. It helps users alleviate the information overload problem by recommending users with the personalized information, products or services (called items). Collaborative filtering and content-based recommendation algorithms have been widely deployed in e-commerce web sites. However, they both suffer from the scalability problem. In addition, there are few suitable similarity measures for the content-based recommendation methods to compute the similarity between items. In this paper, we propose a hybrid recommendation algorithm by combing the content-based and collaborative filtering techniques as well as incorporating the coupled similarity. Our method firstly partitions items into several item groups by using a coupled version of k-modes clustering algorithm, where the similarity between items is measured by the Coupled Object Similarity considering coupling between items. The collaborative filtering technique is then used to produce the recommendations for active users. Experimental results show that our proposed hybrid recommendation algorithm effectively solves the scalability issue of recommender systems and provides a comparable recommendation quality when lacking most of the item features.