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

Collaborative Filtering (CF) is the most widely used prediction technique in Recommendation System (RS). Most of the current CF recommender systems maintains single criteria user rating in user-item matrix. However, recent studies indicate that recommender system depending on multi-criteria can improve prediction and accuracy levels of recommendation by considering the user preferences in multi aspects of items. This gives birth to Multi-Criteria Collaborative Filtering (MC-CF). In MC-CF users provide the rating on multiple aspects of an item in new dimensions, thereby increasing the size of rating matrix, sparsity and scalability problem. Appropriate dimensionality reduction techniques are thus needed to take care of these challenges to reduce the dimension of user-item rating matrix to improve the prediction accuracy and efficiency of CF recommender system. The process of dimensionality reduction maps the high dimensional input space into lower dimensional space. Thus, the objective of this paper is to propose an efficient MC-CF algorithm using dimensionality reduction technique to improve the recommendation quality and prediction accuracy. Dimensionality reduction techniques such as Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are used to solve the scalability and alleviate the sparsity problems in overall rating. The proposed MC-CF approach will be implemented using Apache Mahout, which allows processing of massive dataset stored in distributed/non-distributed file system. KeywordsRecommendation System; Multi-Criteria Collaborative Filtering; Singular Value Decomposition; Principal Component Analysis; Mahout