🎬 Movie Recommendation Knowledge-Based System

📌 Project Information

A knowledge-based movie recommendation system built using Prolog and expert system concepts.

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👨‍💻 Project Overview

This project is a Prolog-based Movie Recommendation System that uses a knowledge-based expert system approach to provide personalized movie recommendations. The system uses a knowledge-based expert system approach to provide personalized movie recommendations for users.

The recommendation engine combines:

• Genre-based recommendation
• Director-based recommendation
• Collaborative filtering
• User similarity analysis

The project demonstrates how Artificial Intelligence concepts and logical reasoning can be implemented using Prolog.

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🎯 Objectives

The main objectives of this project are:

• To build a movie recommendation expert system using Prolog
• To implement logical rules for intelligent decision making
• To analyze user preferences and ratings
• To generate personalized movie suggestions
• To demonstrate collaborative filtering concepts

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⚙️ Technologies Used

Technology	Purpose
Prolog	Knowledge representation and logical reasoning
SWI-Prolog	Running and testing the system
GitHub	Version control and project hosting

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🧠 System Features

✅ Movie Knowledge Base

Stores movie information including:

• Movie name
• Genre
• Director

Example:

movie(inception, scifi, nolan).

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✅ User Preferences

Stores:

• Preferred genres
• Preferred directors

Example:

prefers_genre(user_a, scifi).
prefers_director(user_a, nolan).

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✅ Ratings System

Users can rate movies on a scale from 1–5.

Example:

rating(user_a, inception, 4).

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✅ Genre-Based Recommendation

Suggests movies based on the user's favorite genres.

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✅ Director-Based Recommendation

Suggests movies directed by preferred directors.

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✅ Collaborative Filtering

The system identifies users with similar tastes by comparing ratings and recommends highly rated movies from similar users.

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✅ Similarity Analysis

Calculates:

• Shared movies between users
• Rating similarity
• Average similarity score

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📂 Project Structure

Movie-Recommendation-System/
│
├── prolog.pl
├── LICENSE
└── README.md

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▶️ How to Run

Step 1: Install SWI-Prolog

Download from: https://www.swi-prolog.org/

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Step 2: Open SWI-Prolog

Load the Prolog file:

[prolog].

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Step 3: Run Queries

Genre Recommendation

recommend_genre(user_a, Movie).

Director Recommendation

recommend_director(user_a, Movie).

Collaborative Recommendation

recommend_collaborative(user_a, Movie).

All Recommendations

recommend(user_a, Movie).

Recommendations Without Duplicates

recommend_no_duplicates(user_a, Movie).

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📊 Sample Output

Example:

?- recommend_director(user_a, Movie).

Movie = inception ;
Movie = interstellar ;
Movie = dark_knight.

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🚧 Challenges Faced

• Managing recommendation duplicates
• Handling sparse rating data
• Calculating user similarity efficiently
• Maintaining modular and reusable rules

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📚 AI Concepts Used

• Knowledge Representation
• Rule-Based Expert System
• Logical Inference
• Collaborative Filtering
• Similarity Measurement

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📖 Conclusion

The project successfully demonstrates how Artificial Intelligence techniques and logical programming can be used to create an intelligent movie recommendation system. The modular design allows easy scalability by adding more users, movies, and recommendation rules in the future.

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👨‍🎓 Author

Md Tausif Uddin

Department of Computer Science & Engineering (CSE)
University of Asia Pacific (UAP)

GitHub: https://github.com/tausif112

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📜 License

This project is licensed under the MIT License.