An intelligent film recommender system based on emotional analysis
The existing personalized film recommendation methods take the user’s historical rating as an important basis for recommendation. However, the user’s rating standards are different, so it is difficult to mine the user’s real preferences and form accurate push. Therefore, to achieve high-quality pers...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-03-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1243.pdf |
| _version_ | 1811154543721840640 |
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| author | Wenzuixiong Xiong Yichao Zhang |
| author_facet | Wenzuixiong Xiong Yichao Zhang |
| author_sort | Wenzuixiong Xiong |
| collection | DOAJ |
| description | The existing personalized film recommendation methods take the user’s historical rating as an important basis for recommendation. However, the user’s rating standards are different, so it is difficult to mine the user’s real preferences and form accurate push. Therefore, to achieve high-quality personalized recommendation of films, it is particularly important to mine the emotion of user reviews. In this article, a personalized recommendation method based on sentiment analysis of film reviews is proposed, where natural language processing technology is used to mine the emotional tendency of user reviews. The multi-modal emotional features are weighted and the weighted fusion feature vector after PSO is taken as the overall emotion vector, then the emotional similarity of weighted fusion is calculated by considering the time factor of content publishing and the average emotional tendency of users. By calculating the matching degree of emotional value between users and films, the top-N film recommendation for target users is given. The test results show that the effect of the personalized film recommendation system based on multimodality is superior to that of the comparison method, which effectively solves the problem of different user rating scales, and really increases users’ interest in watching films. |
| first_indexed | 2024-04-10T04:18:40Z |
| format | Article |
| id | doaj.art-c17a4eba84ee466e931ce650c9ff59b0 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-10T04:18:40Z |
| publishDate | 2023-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-c17a4eba84ee466e931ce650c9ff59b02023-03-11T15:05:07ZengPeerJ Inc.PeerJ Computer Science2376-59922023-03-019e124310.7717/peerj-cs.1243An intelligent film recommender system based on emotional analysisWenzuixiong Xiong0Yichao Zhang1School of art, Hubei University, Wuhan, Hubei, ChinaNational Financing Guarantee Fund, Beijing, Beijing, ChinaThe existing personalized film recommendation methods take the user’s historical rating as an important basis for recommendation. However, the user’s rating standards are different, so it is difficult to mine the user’s real preferences and form accurate push. Therefore, to achieve high-quality personalized recommendation of films, it is particularly important to mine the emotion of user reviews. In this article, a personalized recommendation method based on sentiment analysis of film reviews is proposed, where natural language processing technology is used to mine the emotional tendency of user reviews. The multi-modal emotional features are weighted and the weighted fusion feature vector after PSO is taken as the overall emotion vector, then the emotional similarity of weighted fusion is calculated by considering the time factor of content publishing and the average emotional tendency of users. By calculating the matching degree of emotional value between users and films, the top-N film recommendation for target users is given. The test results show that the effect of the personalized film recommendation system based on multimodality is superior to that of the comparison method, which effectively solves the problem of different user rating scales, and really increases users’ interest in watching films.https://peerj.com/articles/cs-1243.pdfFilm reviewPAD emotional modelPSOMovie recommendationMicro-blog |
| spellingShingle | Wenzuixiong Xiong Yichao Zhang An intelligent film recommender system based on emotional analysis PeerJ Computer Science Film review PAD emotional model PSO Movie recommendation Micro-blog |
| title | An intelligent film recommender system based on emotional analysis |
| title_full | An intelligent film recommender system based on emotional analysis |
| title_fullStr | An intelligent film recommender system based on emotional analysis |
| title_full_unstemmed | An intelligent film recommender system based on emotional analysis |
| title_short | An intelligent film recommender system based on emotional analysis |
| title_sort | intelligent film recommender system based on emotional analysis |
| topic | Film review PAD emotional model PSO Movie recommendation Micro-blog |
| url | https://peerj.com/articles/cs-1243.pdf |
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