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http://dx.doi.org/10.9717/kmms.2019.22.4.443

PARAFAC Tensor Reconstruction for Recommender System based on Apache Spark  

Im, Eo-Jin (Dept. of Computer Science & Engineering., Graduate School, Ewha Womans University)
Yong, Hwan-Seung (Dept. of Computer Science & Engineering., Graduate School, Ewha Womans University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.4, 2019 , pp. 443-454 More about this Journal
Abstract
In recent years, there has been active research on a recommender system that considers three or more inputs in addition to users and goods, making it a multi-dimensional array, also known as a tensor. The main issue with using tensor is that there are a lot of missing values, making it sparse. In order to solve this, the tensor can be shrunk using the tensor decomposition algorithm into a lower dimensional array called a factor matrix. Then, the tensor is reconstructed by calculating factor matrices to fill original empty cells with predicted values. This is called tensor reconstruction. In this paper, we propose a user-based Top-K recommender system by normalized PARAFAC tensor reconstruction. This method involves factorization of a tensor into factor matrices and reconstructs the tensor again. Before decomposition, the original tensor is normalized based on each dimension to reduce overfitting. Using the real world dataset, this paper shows the processing of a large amount of data and implements a recommender system based on Apache Spark. In addition, this study has confirmed that the recommender performance is improved through normalization of the tensor.
Keywords
Tensor Reconstruction; Recommender System; PARAFAC Decomposition; Multi-dimensional Recommendation;
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Times Cited By KSCI : 1  (Citation Analysis)
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