An Emotion-Based movie recommendation system using convolutional neural network
With the growing number of movies released each year, movie recommender systems are becoming more and more important in providing recommendations to users efficiently. However, one of the challenges in developing the recommendation system is the system itself must be accurately enough to record the...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/4692/1/fyp_CS_2022_CKS.pdf http://eprints.utar.edu.my/4692/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833428676691099648 |
|---|---|
| author | Chong, Kok Sian |
| author_facet | Chong, Kok Sian |
| author_sort | Chong, Kok Sian |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | With the growing number of movies released each year, movie recommender systems are becoming more and more important in providing recommendations to users efficiently. However, one of the challenges in developing the recommendation system is the system itself must be accurately enough to record the user's profile, in order to determine their movie preferences. Therefore, a web-based movie recommendation system with Convolutional Neural Network (CNN) model is proposed in this study, to recognize human emotion from facial images. The movie recommendation process will be carried out by capturing the user’s emotion in order to save their searching time rather than manually going through all of the movies to find and pick the most suited one. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.4692 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2022 |
| record_format | eprints |
| spelling | my-utar-eprints.46922023-01-15T13:50:30Z An Emotion-Based movie recommendation system using convolutional neural network Chong, Kok Sian T Technology (General) With the growing number of movies released each year, movie recommender systems are becoming more and more important in providing recommendations to users efficiently. However, one of the challenges in developing the recommendation system is the system itself must be accurately enough to record the user's profile, in order to determine their movie preferences. Therefore, a web-based movie recommendation system with Convolutional Neural Network (CNN) model is proposed in this study, to recognize human emotion from facial images. The movie recommendation process will be carried out by capturing the user’s emotion in order to save their searching time rather than manually going through all of the movies to find and pick the most suited one. 2022-09-18 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4692/1/fyp_CS_2022_CKS.pdf Chong, Kok Sian (2022) An Emotion-Based movie recommendation system using convolutional neural network. Final Year Project, UTAR. http://eprints.utar.edu.my/4692/ |
| spellingShingle | T Technology (General) Chong, Kok Sian An Emotion-Based movie recommendation system using convolutional neural network |
| title | An Emotion-Based movie recommendation system using convolutional neural network
|
| title_full | An Emotion-Based movie recommendation system using convolutional neural network
|
| title_fullStr | An Emotion-Based movie recommendation system using convolutional neural network
|
| title_full_unstemmed | An Emotion-Based movie recommendation system using convolutional neural network
|
| title_short | An Emotion-Based movie recommendation system using convolutional neural network
|
| title_sort | emotion-based movie recommendation system using convolutional neural network |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/4692/1/fyp_CS_2022_CKS.pdf http://eprints.utar.edu.my/4692/ |
| url_provider | http://eprints.utar.edu.my |