Building a content recommendation system for MYTV services of VNPT | |
| Author | Dinh Thi Nhan |
| Call Number | AIT Project no.PMDS-23-04 |
| Subject(s) | Recommender systems (Information filtering) Machine learning Television--Vietnam--Data processing |
| Note | A project study submitted in partial fulfillment of the requirements for the degree of Professional Master in Data Science and Artificial Intelligence Applications |
| Publisher | Asian Institute of Technology |
| Abstract | Currently, there are too many television programs airing, making it difficult for viewers to choose. With hundreds of different programs, viewers have countless options, but this also makes it challenging for them to find their favorite shows. Offering content suggestion serves as a way to tackle the mentioned issue. MyTV's system, until April 2020, didn't personalize recommendations. Its first attempt in 2021 faced data and algorithm issues, leading to customer dissatisfaction and risk of revenue loss. This project brings solutions to help MyTV address this issue. Hence, the primary aim of this project is to improve the system's recommendation algorithm. The aim is to create a more personalized, user-centric experience, enhancing customer satisfaction and ultimately, the potential for increased revenue for MyTV. This will be achieved through detailed data analysis, improved programming strategies, and advanced machine learning techniques. Using the customer data and their usage history supplied to the big data system, the project applies superior data processing methods and machine learning models to present the most appropriate content recommendations tailored to the preferences, habits, and behaviors of every single customer or customer group. This helps customers find the content they need accurately. The recommended content spans across various services offered by MyTV, including television services, film and series services, and comprehensive entertainment services. The content recommendations are also based on multiple criteria, such as behavioral criteria, content criteria, and preference criteria. |
| Year | 2023 |
| Type | Project |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Professional Master in Data Science and Artificial Intelligence Applications (PMDS) |
| Chairperson(s) | Vatcharaporn Esichaikul;Chutiporn Anutariya (Co-Chairperson) |
| Examination Committee(s) | Chaklam Silpasuwanchai;Chantri Polprasert |
| Scholarship Donor(s) | AIT Scholarships |
| Degree | Professional Master in Data Science and Artificial Intelligence Applications - Asian Institute of Technology, 2023 |