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Movie Recommendation through Multiple Bias Analysisopen access

Authors
Hwang, Tae-GyuKim, Sung Kwon
Issue Date
Mar-2021
Publisher
MDPI
Keywords
rating prediction; collaborative filtering; movie recommendation; bias analysis
Citation
APPLIED SCIENCES-BASEL, v.11, no.6
Journal Title
APPLIED SCIENCES-BASEL
Volume
11
Number
6
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/51360
DOI
10.3390/app11062817
ISSN
2076-3417
2076-3417
Abstract
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users' and items' biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.
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