  Anime is quite well-received today, especially among the younger generations.
With many genres of available shows, more and more people are increasingly
getting attracted to this niche section of the entertainment industry. As anime
has recently garnered mainstream attention, we have insufficient information
regarding users' penchant and watching habits. Therefore, it is an uphill task
to build a recommendation engine for this relatively obscure entertainment
medium. In this attempt, we have built a novel hybrid recommendation system
that could act both as a recommendation system and as a means of exploring new
anime genres and titles. We have analyzed the general trends in this field and
the users' watching habits for coming up with our efficacious solution. Our
solution employs deep autoencoders for the tasks of predicting ratings and
generating embeddings. Following this, we formed clusters using the embeddings
of the anime titles. These clusters form the search space for anime with
similarities and are used to find anime similar to the ones liked and disliked
by the user. This method, combined with the predicted ratings, forms the novel
hybrid filter. In this article, we have demonstrated this idea and compared the
performance of our implemented model with the existing state-of-the-art
techniques.
