anime recommendation system research paper

RikoNet: A Novel Anime Recommendation Engine

• Published: 03 March 2023
• Volume 82 , pages 32329–32348, ( 2023 )

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• Badal Soni   ORCID: orcid.org/0000-0002-9617-9468 1 ,
• Debangan Thakuria 1 ,
• Nilutpal Nath 1 ,
• Navarun Das 1 &
• Bhaskarananda Boro 1  

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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.

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Availability of data and material.

The prototype of RikoNet is available online with an introductory tutorial on how to obtain anime recommendations, in our github repository https://github.com/NilutpalNath/RikoNet .

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National Institute of Technology Silchar, Silchar, India

Badal Soni, Debangan Thakuria, Nilutpal Nath, Navarun Das & Bhaskarananda Boro

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Soni, B., Thakuria, D., Nath, N. et al. RikoNet: A Novel Anime Recommendation Engine. Multimed Tools Appl 82 , 32329–32348 (2023). https://doi.org/10.1007/s11042-023-14710-9

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Received : 31 May 2021

Revised : 20 June 2022

Accepted : 04 February 2023

Published : 03 March 2023

Issue Date : September 2023

DOI : https://doi.org/10.1007/s11042-023-14710-9

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