Journal of Computer Sciences and Applications, 2017, Vol. 5, No. 2, 76-82 Available online at http://pubs.sciepub.com/jcsa/5/2/4 ©Science and Education Publishing DOI:10.12691/jcsa-5-2-4 Efficient and Scalable Matrix Factorization Transfer with Review Helpfulness for Massive Data Processing Aboagye Emelia Opoku 1,2,* , Jianbin Gao 3 , Dagadu Joshua Caleb 4 , Qi Xia 5,6 1 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China 2 Kumasi Technical University, Kumasi, Ghana 3 School of Resource and Environment, University of Electronic, Science and Technology of China Chengdu, Chengdu, China 4 Computer Science Department, University of Electronic Science and Technology, Chengdu, China 5 School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu, Chengdu, China 6 Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, China *Corresponding author: eoaboagye@yahoo.co.uk Abstract We explore the sparsity problem associated with recommendation system through the concept of transfer learning (TL) which are normally caused by missing and noisy ratings and or review helpfulness. TL is a machine learning (ML) method which aims to extract knowledge gained in a source task/domain and use it to facilitate the learning of a target predictive function in a different domain. The creation and transfer of knowledge are a basis for competitive advantage. One of the challenges prevailing in this era of big data is scalable algorithms that process the massive data in reducing computational complexity. In the RS field, one of the inherent problems researchers always try to solve is data sparsity. The data associated with rating scores and helpfulness of review scores are always sparse presenting sparsity problems in recommendation systems (RSs). Meanwhile, review helpfulness votes helps facilitate consumer purchase decision-making processes. We use online review helpfulness votes as an auxiliary in formation source and design a matrix transfer framework to address the sparsity problem. We model our Homogenous Fusion Transfer Learning approach based on Matrix Factorization HMT with review helpfulness to solve sparsity problem of recommender systems and to enhance predictive performance within the same domain. Our experiments show that, our framework Efficient Matrix Transfer Learning (HMT) is scalable, computationally less expensive and solves the sparsity problem of recommendations in the e-commerce industry. Keywords: fusion, transfer learning, sparsity, helpfulness Cite This Article: Aboagye Emelia Opoku, Jianbin Gao, Dagadu Joshua Caleb, and Qi Xia, “Efficient and Scalable Matrix Factorization Transfer with Review Helpfulness for Massive Data Processing.” Journal of Computer Sciences and Applications, vol. 5, no. 2 (2017): 76-82. doi: 10.12691/jcsa-5-2-4. 1. Introduction Massive data analytics is aimed at making sense of data by applying efficient and scalable algorithms on big data for its analysis, learning, modelling, visualization and understanding. this requires the design of efficient and effective algorithms and systems to integrate the data and uncover the hidden values from it. It also requires methodologies and algorithms for automatic or mixed-initiative knowledge discovery and learning, data transformation and modelling, predictions and explanations of the data [1,2]. Online purchasing at various e-commerce sites like amazon, Netflix are crowded with chunks of data. Consumer purchasing decision making of such online products are immensely affected by their reviews and its associated information in such massive data environments [3,4], in such a way that, information overload has now become a constraint to consumers daily [5]. In this paper we focus on the sparsity problem that arises with review helpfulness giving rise to sparse matrix. the sparsity problem originates when available data are insufficient for identifying similar users or we can say neighbours and it is a major issue since it limits the quality of recommendations and the applicability of collaborative filtering in general. The main objective of our work is to develop an effective approach that provides high quality recommendations even when sufficient data are unavailable [6]. We use matrix factorization integration to reduce the sparsity using homogenous transfer learning paradigm. This on one hand must provide valuable product related information for consumers which in the long run affect the quality of recommendation system algorithms [7]. Consequently, researchers from diverse fields have explored the most efficient and effective ways of predicting quality recommendations. One of such paradigms in recent times has been transfer learning [8,9,10] from a psychological point of view, transfer learning is the study of the dependency of human conduct, learning, or performance on prior experience [11]. For instance, if one is good at coding in object oriented programming coding, may learn python programming language principally. Our main motivation for application of transfer learning in the recommender system field is the need for machine