RikoNet: A Novel Anime Recommendation Engine

Anime is quite well-received today, especially among the younger generations. 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.

Automated summary

Anime is popular nowadays, especially among the younger generations. Building a recommendation engine for this relatively obscure entertainment medium is challenging due to insufficient knowledge on users' preferences and watching habits. In this study, we developed a novel hybrid recommendation system that can serve as both a recommendation system and a way to explore new anime genres and titles. Our solution utilizes deep autoencoders for predicting ratings and generating embeddings, and utilizes clusters formed by these embeddings to find similar anime titles liked or disliked by the user. We demonstrated the effectiveness of our approach and compared it to existing state-of-the-art techniques.