Contents Preference Model Combined with Matrix Factorization for Movie Recommendation

Seoin Baek; Daiki Min

doi:10.7232/JKIIE.2021.47.3.280

Contents Preference Model Combined with Matrix Factorization for Movie Recommendation

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콘텐츠 선호 모형을 결합한 행렬 분해 기반 영화 추천시스템

Type: Academic journal

Author: Seoin Baek (이화여자대학교) Daiki Min (이화여자대학교)

Journal: Korean Institute Of Industrial Engineers Journal of the Korean Institute of Industrial Engineers Vol.47 No.3 KCI Excellent Accredited Journal

Published: 2021.6

Pages: 280 - 288 (9page)

DOI: 10.7232/JKIIE.2021.47.3.280

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Contents Preference Model Combined with Matrix Factorization for Movie Recommendation

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2021

Contents Preference Model Combined with Matrix Factorization for Movie Recommendation

Seoin Baek , Daiki Min Journal of the Korean Institute of Industrial Engineers 2021.06 Academic journal

Two-stage Contents Preference Model Combined with Matrix Factorization for Movie Recommendation

백서인 2021.01 Thesis

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With the growth of the media market, companies that provide contents services such as movies, music and video are providing various content to satisfy users. While these changes have allowed users to enjoy richer content, a new problem has emerged that they have to spend much more time than before to find content that suits their taste among the overflowing content. Recommender system has become an important key to solve these problems. Matrix Factorization (MF) is the most well-known and widely used for identifying users’ preference on contents. However, MF has a drawback of data sparsity and is not capable of utilizing meta-data. In this study, we proposed a two-stage contents preference model with Matrix Factorization (CPMF). The proposed method combines MF and contents preference models that utilize a variety of meta-data (e.g., actors, directors, and genres) to identify users’ preferences. A numerical analysis is conducted to evaluate the performance of the proposed method for movie recommendation domains.

#Movie Recommender System #Matrix Factorization #Contents Preference Model

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Chen, H. W., Wu, Y. L., Hor, M. K., and Tang, C. Y. (2017), Fully Contentbased Movie Recommender System with Feature Extraction Using Neural Network, In 2017 International Conference on Machine Learning and Cybernetics(ICMLC), 2, 504-509.

Chen, S. and Peng, Y. (2018), Matrix Factorization for Recommendation with Explicit and Implicit Feedback, Knowledge-Based Systems, 158, 109-117.

Choi, S. M., Ko, S. K., and Han, Y. S. (2012), A Movie Recommendation Algorithm based on Genre Correlations, Expert Systems with Applications, 39(9), 8079-8085.

Deldjoo, Y., Schedl, M., and Elahi, M. (2019), Movie Genome Recommender : A Novel Recommender System based on Multimedia Content, In 2019 International Conference on Content-Based Multimedia Indexing(CBMI), 1-4.

Diao, Q., Qiu, M., Wu, C. Y., Smola, A. J., Jiang, J., and Wang, C. (2014), Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation(JMARS), In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 193-202.

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