Improving Graph-Based Movie Recommender System Using Cinematic Experience
With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past pre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/3/1493 |
| _version_ | 1797489070839955456 |
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| author | CheonSol Lee DongHee Han Keejun Han Mun Yi |
| author_facet | CheonSol Lee DongHee Han Keejun Han Mun Yi |
| author_sort | CheonSol Lee |
| collection | DOAJ |
| description | With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation. |
| first_indexed | 2024-03-10T00:12:14Z |
| format | Article |
| id | doaj.art-94323bc8144145d197cf8e2f0b3cbc38 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:12:14Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-94323bc8144145d197cf8e2f0b3cbc382023-11-23T15:58:31ZengMDPI AGApplied Sciences2076-34172022-01-01123149310.3390/app12031493Improving Graph-Based Movie Recommender System Using Cinematic ExperienceCheonSol Lee0DongHee Han1Keejun Han2Mun Yi3Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, KoreaDepartment of Data Engineering, Buzzvil, Seoul 05623, KoreaIntelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, KoreaDepartment of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, KoreaWith the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation.https://www.mdpi.com/2076-3417/12/3/1493graph neural networksrecommender systemsentiment analysisemotion analysisBERTnatural language processing |
| spellingShingle | CheonSol Lee DongHee Han Keejun Han Mun Yi Improving Graph-Based Movie Recommender System Using Cinematic Experience Applied Sciences graph neural networks recommender system sentiment analysis emotion analysis BERT natural language processing |
| title | Improving Graph-Based Movie Recommender System Using Cinematic Experience |
| title_full | Improving Graph-Based Movie Recommender System Using Cinematic Experience |
| title_fullStr | Improving Graph-Based Movie Recommender System Using Cinematic Experience |
| title_full_unstemmed | Improving Graph-Based Movie Recommender System Using Cinematic Experience |
| title_short | Improving Graph-Based Movie Recommender System Using Cinematic Experience |
| title_sort | improving graph based movie recommender system using cinematic experience |
| topic | graph neural networks recommender system sentiment analysis emotion analysis BERT natural language processing |
| url | https://www.mdpi.com/2076-3417/12/3/1493 |
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