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Collaborative Filtering is the most widely used prediction technique inRecommendation System. Most of the current CF recommender systems maintainssingle criteria user rating in user item matrix. However, recent studiesindicate that recommender system depending on multi criteria can improveprediction and accuracy levels of recommendation by considering the userpreferences in multi aspects of items. This gives birth to Multi CriteriaCollaborative Filtering. In MC CF users provide the rating on multiple aspectsof an item in new dimensions,thereby increasing the size of rating matrix,sparsity and scalability problem. Appropriate dimensionality reductiontechniques are thus needed to take care of these challenges to reduce thedimension of user item rating matrix to improve the prediction accuracy andefficiency of CF recommender system. The process of dimensionality reductionmaps the high dimensional input space into lower dimensional space. Thus, theobjective of this paper is to propose an efficient MC CF algorithm usingdimensionality reduction technique to improve the recommendation quality andprediction accuracy. Dimensionality reduction techniques such as Singular ValueDecomposition and Principal Component Analysis are used to solve thescalability and alleviate the sparsity problems in overall rating. The proposedMC CF approach will be implemented using Apache Mahout, which allows processingof massive dataset stored in distributed/non-distributed file system.

An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework