Publication: SGD-Rec: A Matrix Decomposition Based Model for Personalized Movie Recommendation
Issued Date
2020-06-01
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2-s2.0-85091886396
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Mahidol University
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SCOPUS
Bibliographic Citation
17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020. (2020), 588-591
Suggested Citation
Siripen Pongpaichet, Thatchapon Unprasert, Suppawong Tuarob, Petch Sajjacholapunt SGD-Rec: A Matrix Decomposition Based Model for Personalized Movie Recommendation. 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020. (2020), 588-591. doi:10.1109/ECTI-CON49241.2020.9158308 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/59944
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SGD-Rec: A Matrix Decomposition Based Model for Personalized Movie Recommendation
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Abstract
© 2020 IEEE. A personalized recommendation has been an active area of research. Many companies such as Facebook, Amazon, and eBay have incorporated such functionality to enhance user experience and engagement. In today's market, streaming digital contents (e.g., online movies) have become ubiquitous and accessi-ble from anywhere and anytime. The rapid growth of streaming market urges many providers to offer a personalized experience to capture customer loyalty. In this paper, we present a movie recommending system based on our proposed rating prediction algorithm using singular value decomposition (SVD). Empirical evaluation is conducted on two tasks: rating prediction and movie recommendation, using two case studies from MovieLens and Thaiware Movie.