Improving Graph-Based Movie Recommender System Using Cinematic Experience

Cited 7 time in webofscience Cited 0 time in scopus
  • Hit : 182
  • Download : 0
With the advent of many movie content platforms, users face a flood of content and conse-quent 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.
Publisher
MDPI
Issue Date
2022-02
Language
English
Article Type
Article
Citation

APPLIED SCIENCES-BASEL, v.12, no.3

ISSN
2076-3417
DOI
10.3390/app12031493
URI
http://hdl.handle.net/10203/292105
Appears in Collection
IE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 7 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0