  Video games have become an integral part of most people's lives in recent
times. This led to an abundance of data related to video games being shared
online. However, this comes with issues such as incorrect ratings, reviews or
anything that is being shared. Recommendation systems are powerful tools that
help users by providing them with meaningful recommendations. A straightforward
approach would be to predict the scores of video games based on other
information related to the game. It could be used as a means to validate
user-submitted ratings as well as provide recommendations. This work provides a
method to predict the G-Score, that defines how good a video game is, from its
trailer (video) and summary (text). We first propose models to predict the
G-Score based on the trailer alone (unimodal). Later on, we show that
considering information from multiple modalities helps the models perform
better compared to using information from videos alone. Since we couldn't find
any suitable multimodal video game dataset, we created our own dataset named
VGD (Video Game Dataset) and provide it along with this work. The approach
mentioned here can be generalized to other multimodal datasets such as movie
trailers and summaries etc. Towards the end, we talk about the shortcomings of
the work and some methods to overcome them.
