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.