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.
This section is devoted to briefly outline the surveys concerning recommender systems. Recommender systems that combine different recommendation techniques are presented in one of the first surveys @cite_102 . A comparison among the different recommendation techniques is provided and their advantages and disadvantages are discussed. Also, the different hybridization methods are described. The existing hybrid approaches are briefly presented and a hybrid recommender system that combines knowledge-based recommendation and collaborative filtering is introduced. Experiments are conducted on the proposed recommender system using data derived from the web server's log. This survey proved that there were many combinations of techniques to be explored and outlined the needs of the field of hybrid recommender systems.
@cite_102
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Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.