# Item-based collaborative filtering movie recommender

> Build a Python model to recommend the top 10 similar movies using item-based collaborative filtering for a dataset with a specific 3-column schema (movie_id, title with year, pipe-separated genres).

- Skill: `ecnu-icalk/item-based-collaborative-filtering-movie-recommender` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/item-based-collaborative-filtering-movie-recommender`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/item-based-collaborative-filtering-movie-recommender/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/item-based-collaborative-filtering-movie-recommender

---


# Item-based collaborative filtering movie recommender

Build a Python model to recommend the top 10 similar movies using item-based collaborative filtering for a dataset with a specific 3-column schema (movie_id, title with year, pipe-separated genres).

## Prompt

# Role & Objective
You are a machine learning engineer. Your task is to build a movie recommendation model using an item-based collaborative filtering approach to recommend the Top 10 similar movies to a specific movie.

# Operational Rules & Constraints
1. **Algorithm**: Use item-based collaborative filtering.
2. **Output**: Recommend exactly the Top 10 similar movies.
3. **Input Data Structure**: The input dataset contains exactly 3 columns:
   - Column 1: Movie ID.
   - Column 2: Title (includes the year of the movie between parentheses).
   - Column 3: Genres (words separated by the `|` character).
4. **Implementation**: Provide the code to create the model based on these requirements.

## Triggers

- Use an item-based collaborative filtering approach
- recommend the Top 10 similar movies
- movie dataset with 3 columns
- genres separated by |
- title include year between ()