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SGD-Rec: A Matrix Decomposition Based Model for Personalized Movie Recommendation

  • Mahidol University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages588-591
Number of pages4
ISBN (Electronic)9781728164861
DOIs
Publication statusPublished - Jun 2020
Event17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020 - Virtual, Online, Thailand
Duration: 24 Jun 202027 Jun 2020

Publication series

Name17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020

Conference

Conference17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020
Country/TerritoryThailand
CityVirtual, Online
Period24/06/2027/06/20

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