Impulsion of Movie’s Content-Based Factors in Multi-modal Movie Recommendation System

Nowadays the Recommendation System, a subclass of information filtering system does not require any introduction, and the movie recommendation system plays an important role in the streaming platform where a huge number of movies are required to be analyzed before showcasing a perfectly matched subset of them to its users. The existing works in this domain focus only on the output and consider the model’s input similar for all users. But actually, the movie embedding input vector varies on a user basis. A user’s perception of a movie depends on the movie’s genre as well as its meta information (story, director, and cast). To formulate the fact, we have introduced two scores, (i) TextLike_score (TL_score) and (ii) GenreLike_score (GL_score). Our proposed Cross-Attention-based Model outperforms the SOTA (state-of-the-art) by leveraging the effect of the scores and satisfying our factual notion. In this paper, we have evaluated our model’s performance over two different datasets, (i) MovieLens-100K(ML-100K) and (ii) MFVCD-7K. Regarding multi-modality, the audio-video information of movies’ are used and textual information has been employed for score calculation. Finally, it is experimentally proved that the Cross-Attention-based multi-modal movie recommendation system with the proposed Meta_score successfully covers all the analytical queries supporting the purpose of the experiment.