Context

This dataset contains a total of 16737 unique animes. The reason for creating this dataset is the requirement of a clean dataset of Anime. I found a few datasets on anime, most of the datasets had the major anime but some dataset 1) doesn't have 'Genre' or 'Synopsis' of anime. For content-based recommendation, it is helpful if we have more information about anime 2) have duplicate data 3) missing data is represented by different notations.

Content

Anime_id :anime Id (as per myanimelist.net) Title : name of anime Genre :Main genre
Synopsis :Brief Discription Type
Producer Studio Rating :Rating of anime as pe myanimelist.net/ ScoredBy : Total no user scored given anime Popularity :Rank of anime based on popularity Members :No of members added given anime on their list Episodes : No. of episodes Source
Aired Link

Acknowledgements

This dataset is a combination of 2 datasets

1. https://docs.google.com/spreadsheets/d/1brguO5nGfXS-Fr1Xcf3pqPTQoBUPGLTYM_EMAA9yJFw/export?format=csv&id=1brguO5nGfXS-Fr1Xcf3pqPTQoBUPGLTYM_EMAA9yJFw&gid=0
2. https://www.kaggle.com/CooperUnion/anime-recommendations-database

Anime Recommendation Dataset (2,000 Titles + 250 Users)

This dataset is designed for building and testing anime recommendation systems. It contains 2,000 anime titles and 250 virtual users with generated preference data, making it ideal for both content-based and collaborative filtering approaches.

Files Included

animes.csv

The main metadata file containing information for each anime.

Dataset Overview

anime_genre_binary.csv — One-hot encoded version of the dataset.

This file is more convenient for training machine learning models.

Dataset Overview

Genre Information

The dataset covers 76 genres, offering rich diversity for modeling. Genres include but are not limited to:

Action, Adventure, Comedy, Drama, Fantasy, Mecha, Romance, Sci-Fi, Slice of Life, Supernatural, and many more.

(Full list of 76 genres is included in the dataset metadata.)

list_of_users.csv

This file represents synthetic user profiles, each describing their preference intensity for each genre. Useful for modeling user embeddings or computing user–genre similarity.

training_data.csv

The user–anime rating matrix used for collaborative filtering or hybrid recommendation.

Use Cases

-Content-based or hybrid anime recommendation systems -Clustering or similarity analysis based on genre and synopsis -NLP tasks such as synopsis embedding or sentiment classification -Exploratory Data Analysis (EDA) and visualization

Notes

-All data are cleaned and preprocessed for ease of use. -Missing values have been handled appropriately. -The one-hot encoded version is optimized for ML pipelines.

Hello Everyone This dataset consist of detailed information about Animes. The dataset consist of different columns namely: - Name - Type - Rating - Rank - Description - Tags - NTags This dataset can be used for various purposes including Recommendation Engines and Search Engines. I have used the data for making a Search Engine that can be accessed here https://flask-production-16d0.up.railway.app/

This dataset consists of user ratings for anime titles. Each user in the dataset has provided at least 5 ratings, ensuring a minimum level of engagement. The dataset includes user anime ratings and detailed information about anime, making it suitable for tasks such as recommendation systems and genre-based filtering. Dataset is freshly-created so it cover newer animes. Data is provided in the MovieLens format except timestamp column. With minor modifications, the dataset can be used in any recommendation project that utilizes the MovieLens dataset. I was able to train BERT model in https://github.com/jaywonchung/BERT4Rec-VAE-Pytorch project with some small modifications.Some StatisticsNumber of Users: 1,774,522Number of Animes: 20,237Total Ratings: 148,170,496BERT Anime Recommender GitHub repo : https://github.com/MRamazan/AnimeRecBERTDataset GitHub repo: https://github.com/MRamazan/User-Animelist-DatasetWeb demo: https://www.animerecbert.online (may be down)

This dataset offers a comprehensive overview of the top animes of 2024, and is useful for building recommendation systems, visualizing trends in anime popularity and score, predicting scores and popularity, and such.

Contents

The dataset contains 22 features:

• Score: The rating or score assigned to each anime title.
• Popularity: Measure of how popular each anime is among viewers.
• Rank: Ranking of each anime title within the dataset.
• Members: The number of members or viewers associated with each anime.
• Description: A brief overview or summary of the plot and themes of each anime.
• Synonyms: Alternative titles or synonyms used for each anime.
• Japanese Title: Original title of the anime in Japanese.
• English Title: English-translated title of the anime.
• Type: Classification of anime type (e.g., TV series, movie, OVA, etc.).
• Eps: Total number of episodes in each anime series.
• Status: Current status of the anime (e.g., ongoing, completed, etc.).
• Aired: Date range of when the anime was aired.
• Premiered: Date when the anime premiered for the first time.
• Broadcast: Information about the broadcasting platform or channel.
• Producers: Companies or studios involved in producing the anime.
• Licensors: Organizations or companies holding the licensing rights for the anime.
• Studios: Animation studios responsible for producing the anime.
• Source: Original source material for the anime (e.g., manga, novel, original).
• Genres: Categories or genres that the anime belongs to.
• Demographic: Target demographic audience for the anime (e.g., shounen, shoujo, seinen, josei).
• Duration: Duration of each episode or movie.
• Rating: Content rating assigned to each anime (e.g., G, PG, PG-13, R).

Acknowledgements

All of the information in this dataset has been gathered by scraping the MyAnimeList website, and is available under the Creative Commons License.

Cover Photo by: Playground.ai

This repository contains three datasets for evaluating accuracy, miscalibration and popularity lift in recommender systems. All datasets contain genre/category information in addition to different user group splits:

Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)

MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)

MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)

'user_events_cats.txt' contains the users' rating/interaction data along with a list of genres/categories assigend to the rated items. The list of categories is given in 'categories.txt'. Additionally, assignments to three user groups that differ in their inclination to popular/mainstream items are provided: LowPop in 'low_main_users.txt', MedPop in 'med_main_users.txt', and HighPop in 'high_main_users.txt'.

The format of the three user files are "user,mainstreaminess"

The format of the user-events files are "user,item,preference,cats", where different categories are separated by '|'

The format of the categories files are "category-name,index", where index refers to the category-id in the user-events files

Example Python-code for analyzing the datasets as well as empirical results on calibration, popularity lift and accuracy can be found on GitHub: https://github.com/domkowald/FairRecSys

Description

Our dataset comprises comprehensive user preference data gathered from 73,516 avid anime enthusiasts, spanning across 12,294 diverse anime titles. Each individual user has the autonomy to curate their own completed anime list, supplemented with personal ratings reflecting their viewing experience. This rich compilation of user-generated ratings forms the backbone of our dataset, offering invaluable insights into the nuanced preferences and tastes of anime enthusiasts worldwide.

Key Features

User Profiles:-

Explore the preferences and behaviors of over 73,000 users, each with their unique anime consumption habits and rating patterns.

Anime Titles:-

Dive into a vast collection of 12,000+ anime titles, ranging from timeless classics to contemporary releases across various genres and themes.

Completed Lists:-

Gain access to users' completed anime lists, providing a glimpse into the breadth and depth of their viewing history.

Ratings:-

Uncover users' subjective evaluations of anime titles, quantified through personalized ratings, offering a granular understanding of viewer satisfaction and engagement.

Content

Anime.csv

anime_id - unique id identifying an anime. name - full name of anime. genre - comma separated list of genres for this anime. type - movie, TV, OVA, etc. episodes - how many episodes in this show. (1 if movie). rating - average rating out of 10 for this anime. members - number of community members that are in this anime's "group".

Rating.csv

user_id - non identifiable randomly generated user id. anime_id - the anime that this user has rated. rating - rating out of 10 this user has assigned (-1 if the user watched it but didn't assign a rating).

1) Data Introduction • The Top Popular Anime Dataset is a large, open-source dataset containing information about more than 22,000 animated series and movies collected through MyAnimeList's Jikan API.

2) Data Utilization (1) Top Popular Anime Dataset has characteristics that: • This dataset includes unique identifiers, English/Japanese titles, genres, types (TVs, movies, etc.), number of episodes, airing status, start/end date, running time per episode, user rating, rating, age rating, production company, producer, image and trailer URL, synopsis, etc. • For some animations, some values may be missing, such as English titles, ratings, trailers, and end-of-air dates. (2) Top Popular Anime Dataset can be used to: • Development of a recommendation system: It can utilize a variety of information such as user ratings, genres, synopsis, etc. to build a personalized animation recommendation system. • Trend and Genre Analysis: By analyzing popularity and rating changes by time series, genre, and production company, trends and success factors in the animation industry can be derived.

Four multimedia recommender systems datasets to study popularity bias and fairness:

Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)

MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)

BookCrossing (book.zip), based on the BookCrossing dataset of Uni Freiburg (http://www2.informatik.uni-freiburg.de/~cziegler/BX/)

MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)

Each dataset contains of user interactions (user_events.txt) and three user groups that differ in their inclination to popular/mainstream items: LowPop (low_main_users.txt), MedPop (med_main_users.txt), and HighPop (high_main_users.txt).

The format of the three user files are "user,mainstreaminess"

The format of the user-events files are "user,item,preference"

Example Python-code for analyzing the datasets as well as more information on the user groups can be found on Github (https://github.com/domkowald/FairRecSys) and on Arxiv (https://arxiv.org/abs/2203.00376)

📚 Dataset Summary

This dataset features 1,941 anime character images, neatly organized into 322 folders, each representing a different anime series 🎌.

📦 Size of downloaded files: 152 MB 🪄 Size of auto-converted Parquet files: 151 MB 📊 Split: Train only 🎭 Classes: 322 unique anime titles

Perfect for image classification, anime recommendation systems, and visual style analysis! 🎨✨

  🏆 Supported Tasks

🖼️ Image Classification: Predict the anime title based on a… See the full description on the dataset page: https://huggingface.co/datasets/adi2606/Anime_Characters.

Anime Dataset

  Dataset Description

This dataset contains comprehensive information about anime series scraped from MyAnimeList (MAL). It includes detailed metadata about 871 (approx) anime series, making it valuable for various NLP tasks, recommendation systems, and cultural analysis. This dataset has NSFW content.

  Dataset Summary

Anime Entries: 50 anime series with rich metadata Languages: English and Japanese (titles, descriptions) Format: JSONL (JSON Lines)… See the full description on the dataset page: https://huggingface.co/datasets/realoperator42/anime-titles-dataset.

Anime-Planet Character Recommendation

Waifus and husbando dataset

This dataset contains information about 132.028 characters and the preference from 72.629 different users of characters scrapped from anime-planet. In particular, this dataset contain:

• Information about the character like Tags, description, genre, etc.
• HTML with character information to do data scrapping. These files contain information such as name, alias, hate rank, tags, etc.
• The character list per user. Include characters that love and hate.

Warning: this dataset includes information on adult anime characters.

Content

The anime data was scrapped between June 29th and August 14th.

• The "html" folder contain 1 html per character (132.028 different characters). I uploaded 2 files as example to don't increase the size of this dataset. All HTML files are in this link: https://drive.google.com/drive/folders/1Kg0OZ6dEsQuJZVqj1CcTGwDnwp4sNOnW?usp=sharing

• user_characters.csv have the list of all character register by the user with the respective love boolean (means if the user love or hate the character). This dataset contains 12 Million row, 72.629 different animes and 132.028 different characters. The file have the following columns:

1. user_id: non identifiable randomly generated user id.
2. character_id: non identifiable randomly generated character id.
3. loved: True if the user loves the character, False if he hates it.

• characters_metadata.csv contain general information of every character (132.028 different character) like Tags, alias, name, gender, etc. This file have the following columns:

1. ID: non identifiable randomly generated character id.
2. Name: full name of this character.
3. Alias: another way to call the character.
4. Gender: gender of the this character.
5. Hair Color: hair color of this character.
6. Love Rank: love rank based in users preference.
7. Hate Rank: hate rank based in users preference.
8. Eye color: eye color of the character.
9. Birthday: date of his birthday.
10. Blood Type: blood type of the character.
11. Tags: comma separated list of tags for this character.
12. Love Count: how many users love this character.
13. Hate Count: how many users hate this character.
14. Description: short text with description if this character.
15. url: url to the main page of character in Anime Planet.

Acknowledgements

Thanks to: 1. Anime Planet for providing anime data.

Inspiration

1. Experiment with different types of recommended. For instance, collaborative filtering or based on context like Tags, description, etc.

2. Use this information to build a character recommended system.

3. Build another dataset with anime topic.

4. Try to Improve Anime Recommendation Database 2020 with more data of characters from the anime. This need to extract the anime id from every html.

This dataset contains over 50,000 anime entries with key information useful for building recommendation systems.

Data was collected via the AniList GraphQL API, cleaned, and formatted into a CSV file. This dataset is suitable for NLP tasks, recommendation engines, and data visualization projects.

Inspired by the growing interest in anime recommendation models and the lack of comprehensive, high-quality datasets.

📺 Anime Watchers Dataset (1960–2025)

📦 Dataset Size: 10,000 Records📄 Format: CSV📜 License: CC-BY-4.0

  📖 Overview

This dataset contains 10,000 synthetic profiles of anime watchers, spanning from the Classic Era (1960s–1989) to the Modern Era (2010–2025).
It is designed for:

Data Analysis Machine Learning Recommendation Systems Trend Prediction in anime consumption.

  📂 Features

Each record represents an individual anime watcher with detailed… See the full description on the dataset page: https://huggingface.co/datasets/Mikey-TraceGod/Anime-Viewers-Data.

Dataset

This dataset was created by Dimple Bathija

Contents

🎌 Ultimate Anime Dataset (8,248 Entries) | 1917-2025

  A meticulously curated collection spanning 108 years of anime history

Love this dataset and the Anime Receipts concept? You can download the complete project via the links below:

  🚀 Unlock the Full Potential

Product What You Get Get It Here

Tier 1 8,248 Anime Dataset (Parquet)

Tier 2 Full AiMi Recommendation System (Backend + UI)

Tier 3 Ultimate AiMi Recommendation System + AiMi Anime… See the full description on the dataset page: https://huggingface.co/datasets/DivyanshuSingh96/aimi-anime-rag-dataset-sample.

MyAnimeList is a popular online platform that allows users to create a list of anime and manga they have watched or read, rate them, and write reviews. The MyAnimeList dataset available on Kaggle is a collection of information about various anime titles and their corresponding attributes, such as title, genre, rating, popularity, and episode count. The columns include information about the anime title, the type of anime (TV show, movie, OVA, etc.), the genre(s) it belongs to, the studio that produced it, the source material (whether it is an original work or an adaptation), and the season and year of release.

In addition to the basic information, the dataset also includes ratings and popularity metrics, such as the number of users who have rated the anime and the average rating score, as well as the number of members who have added the anime to their list and the number of favorites. Moreover, the dataset includes information about the anime's episodes, duration, and opening and ending themes.

This dataset could be useful for various applications, such as building recommendation systems, conducting research on anime trends, and analyzing the relationship between various attributes (e.g., genre and popularity). Overall, the MyAnimeList dataset is an invaluable resource for anyone interested in anime and manga, and it provides a wealth of information that can be leveraged for various data-driven analyses.

This dataset is a comprehensive collection of anime data, fetched from the MyAnimeList website using the Jikan API. It includes information on the latest animes, making it a valuable resource for up-to-date recommendations.

The dataset is primed for building an anime recommendation system. But you can also perform Exploratory data analysis , Data Cleaning .

Overall, this dataset provides a rich source of information for anime enthusiasts and data scientists alike, offering a solid foundation for developing sophisticated recommendation systems and conducting insightful data analysis. It stands as a testament to the power of data in enhancing user experiences and driving innovation in the entertainment industry.

Context

This data set contains information on user preference data from 108,024 users on 15,221 animes. Each user is able to add anime to their completed list and give it a rating and this data set is a compilation of those ratings. This data set includes animes up to 2020 winter. 108,024 users are targeted at any anime fan around the world between the ages of 14 to 34.

Content

Anime.csv - anime_id - myanimelist.net's unique id identifying an anime. - title - full title name of anime. - genres - comma separated list of genres for this anime. - media - movie, TV, OVA, etc. - episodes - how many episodes in this show. (1 if movie or ova). - rating - average rating out of 10 for this anime. - members - number of community members that are in this anime's "group". - start_date - when this anime started. - season - what season this anime started. - source - manga, light_novel, original, etc.

Rating.csv - user_id - non identifiable randomly generated user id. - anime_id - the anime that this user has rated. - rating - rating out of 10 this user has assigned (0 if the user watched it but didn't assign a rating).

Acknowledgements

Thanks to myanimelist.net API for providing anime data and user ratings, and thanks to CooperUnion(https://www.kaggle.com/CooperUnion/anime-recommendations-database)

Inspiration

Building a better anime recommendation system based only on user viewing history.

Based on : https://www.kaggle.com/datasets/tavuksuzdurum/user-animelist-dataset

Cleaned_animelist: Consists of Anime ID, titles, type of content, year of release, score (average ratings from all the users), amount of episodes, the MyAnimeList URL, and sequel.

ratings_df: unique UserID giving Ratings (/10) to each unique AnimeID, also includes the embeddings for users and animes.

This dataset includes fresh and newer animes available, usable for data analysis and identifying trends.