Efficient and Scalable Matrix Factorization Transfer with Review Helpfulness for Massive Data Processing
Efficient and Scalable Matrix Factorization Transfer with Review Helpfulness for Massive Data Processing
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Date
2017
Authors
Dagadu, Caleb Joshua
Aboagye, Emelia Opoku
Jianbin, Gao
Qi, Xia
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Computer Sciences and Applications, 2017
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.
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Citation
Opoku, A. E., Gao, J., Caleb, D. J., & Xia, Q. (2017). Efficient and Scalable Matrix Factorization Transfer with Review Helpfulness for Massive Data Processing. Journal of Computer Sciences and Applications, 5(2), 76-82.