# RikoNet: A Novel Anime Recommendation Engine

arXiv: https://arxiv.org/abs/2106.12970 · HTML: https://mlsanity.com/paper/2106.12970/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Item2Vec: Neural Item Embedding for Collaborative Filtering](https://mlsanity.com/paper/1603.04259/) (2016)
- [Training Deep AutoEncoders for Collaborative Filtering](https://mlsanity.com/paper/1708.01715/) (2017)
- [Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario](https://mlsanity.com/paper/1709.01584/) (2017)

## Built on by (most cited first, 1 of 1)

- [AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images](https://mlsanity.com/paper/2208.11012/) (2022)

## Abstract (arXiv)

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.