Genre Effect Toward Developing a Multi-Modal Movie Recommendation System in Indian Setting

The Recommendation System (RS) plays a crucial role in various platforms as an information filtering agent however, our literature survey found that there is an unexplored area where the user feedback is in ordinal value and the movies are from the Indian regional language-based. Here, we have intro...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics 2024-02, Vol.70 (1), p.2517-2526
Main Authors: Mondal, Prabir, Kapoor, Pulkit, Singh, Siddharth, Saha, Sriparna, Singh, Jyoti Prakash, Singh, Amit Kumar
Format: Article
Language:English
Subjects:
Audio data
Collaboration
Data mining
Datasets
Feedback
Flickscore dataset
GL-score
hindi movie recommendation system
Literature reviews
Mathematical models
Motion pictures
multi-head cross attention
Predictive models
Recommender systems
User feedback
Visualization
wav2vec
Citations: Items that this one cites
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Summary:The Recommendation System (RS) plays a crucial role in various platforms as an information filtering agent however, our literature survey found that there is an unexplored area where the user feedback is in ordinal value and the movies are from the Indian regional language-based. Here, we have introduced a Multi-head cross-attention-based recommendation system for the Indian language-based multi-modal Hindi movie dataset where user feedback is considered from the three different classes, i) Dislike, ii) Like, and iii) Neutral/Not watched. Here, we have used the Audio-Video information of Hindi movie trailers of the Flickscore dataset. Besides that, we have also investigated the performance of a classification-based model in two factors, (i) GenreLike-score GL-score: (which we have formulated to match the user's preferred genres with the genre of the movie), and (ii) Different Audio/Video embeddings. The performance of different combinations of these factors is tested on different modalities of the dataset and proved that GL-score is supportive in preference prediction. The performance of different keyframes extraction techniques has been investigated, and modality-wise different embedding processes have also been introduced here.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2023.3324009