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README.md
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# 📦 Anime Recommender Dataset (Sentence-BERT Ready)
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This dataset is a cleaned and preprocessed version of the [Top 15,000 Ranked Anime Dataset](https://www.kaggle.com/datasets/quanthan/top-15000-ranked-anime-dataset-update-to-32025) originally published on Kaggle by **Quan Than**. It is specifically prepared to be used for **semantic recommendation systems**, including transformer-based models like **Sentence-BERT**.
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---
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## 📌 Original Dataset
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**Source:** [Kaggle - Top 15,000 Ranked Anime Dataset (updated to Mar 2025)](https://www.kaggle.com/datasets/quanthan/top-15000-ranked-anime-dataset-update-to-32025)
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**Author:** Quan Than
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**License:** Apache 2.0
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---
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## 🔧 Modifications & Enhancements
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The original dataset has been modified for better use in **AI-driven recommendation tasks**:
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### ✅ Column Cleanup
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- Removed excessive trailing spaces and newline artifacts in all fields.
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- Standardized formatting across fields like `genres`, `studios`, `producers`.
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### ✅ Feature Engineering
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- Added a new column called **`combined_features`**, which concatenates the most relevant text fields:
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- `genres`
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- `type`
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- `studios`
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- `producers`
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- `source`
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- `rating`
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- `synopsis`
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This column is intended for use in embedding generation via NLP models (e.g. Sentence-BERT, TfidfVectorizer).
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### ✅ Consistency Fixes
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- Ensured that all text fields are valid UTF-8.
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- Filled missing or null values with empty strings (`''`) for compatibility with model pipelines.
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---
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## 🧠 Use Case
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This dataset is designed to be used in **content-based anime recommender systems**, especially those using **semantic similarity** techniques. Typical workflow:
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1. Load the `combined_features` column.
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2. Generate embeddings using a model like `all-MiniLM-L6-v2` (from `sentence-transformers`).
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3. Use cosine similarity to find top matches.
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4. Filter or rank based on score, popularity, or genre.
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---
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## 🗂 Columns
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| Column | Description |
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|-------------------|-----------------------------------------------------------------------------|
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| `anime_id` | MyAnimeList unique ID |
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| `anime_url` | URL to the anime’s MAL page |
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| `image_url` | Link to the anime’s poster image |
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| `name` | Primary title |
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| `english_name` | English-translated title (if any) |
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| `japanese_names` | Japanese name(s) |
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| `score` | Average user score |
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| `genres` | Comma-separated genres |
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| `synopsis` | Full text description |
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| `type` | Format: TV, Movie, OVA, etc. |
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| `episodes` | Number of episodes |
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| `premiered` | Season/year it first aired |
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| `producers` | Companies involved in production |
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| `studios` | Animation studio(s) |
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| `source` | Original work type (Manga, Light Novel, etc.) |
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| `duration` | Time per episode |
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| `rating` | Content rating (e.g., PG-13, R) |
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| `rank` | Rank on MAL |
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| `popularity` | Popularity rank |
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| `favorites` | Number of users marking it as favorite |
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| `scored_by` | Number of users who rated it |
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| `members` | Number of users in total interested |
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| `combined_features` | Custom field combining genres, studios, synopsis, etc. for embeddings use |
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---
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## 📂 Format
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* File: `anime.csv`
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* Encoding: UTF-8
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* Format: Standard CSV
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* Rows: \~15,000 (depending on filtering)
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* Columns: 23 including `combined_features`
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---
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## 🙌 Author Notes
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This dataset was prepared by:
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**👨💻 Youssef ElNahas — aka *TheVigilante***
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- 🔗 [GitHub Profile](https://github.com/TheRealVigilante)
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- 💬 For collaboration or feedback, feel free to reach out!
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---
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## 🛡️ Disclaimer
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This dataset is a derivative of the publicly shared dataset on Kaggle. Please ensure you review and comply with the original dataset’s license and usage terms.
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