Task-Specific and Graph Convolutional Network based Multi-modal Movie Recommendation System in Indian Setting

Prabir Mondal, Pulkit Kapoor, Siddharth Singh, Sriparna Saha, Jyoti Prakash Singh, Naoyuki Onoe

Abstract: Nowadays the Recommendation System, a subclass of information filtering system does not require any introduction, and the movie recommendation system plays a vital role in the streaming platform where many movies are needed to analyze before showcasing a perfectly matched subset of them to its users. Most of the available datasets contain the rating information of user-movie pairs and this is the reason of regression-based works that predict the rating value for a user-movie pair. We have also found that there is no work on the Indian regional language-based dataset containing no users’ feedback in the rating scale.In this paper, we have introduced a recommendation system for the Indian language-based multi-modal Hindi movies’ dataset where users’ feedback is from the three different classes, i) Dislike, ii) Like, and iii) Neutral. Here, we have used the Flickscore dataset and added the audio-video information of the trailers of its movies for making it multi-modal. Besides that, we have investigated the performance of a classification-based model having two modules, (i) Task-Specific (TS) and (ii) Graph Convolutional Network (GCN). The performance of different combinations of these modules is tested on different modalities of the dataset. We have tested its performance in cold-start scenarios also. Modality wise different embedding processes have been introduced here and the experimental results tried to conclude how the model works in uni-modal, bi-modal, and all-modal information of movies in an information system where no rating information is present.