Intelligent Movie Recommendation System Based on User Preferences and Viewing History MATLAB

Okay, let's outline the project details for an Intelligent Movie Recommendation System using MATLAB, focusing on user preferences and viewing history.

**Project Title:** Intelligent Movie Recommendation System Based on User Preferences and Viewing History

**1. Project Overview**

This project aims to develop a movie recommendation system that provides personalized movie suggestions to users based on their past viewing history and explicit/implicit preferences.  The system will analyze user data and movie attributes to generate accurate and relevant recommendations. MATLAB will be used for data analysis, algorithm implementation, and potentially for a simple user interface (though a web-based interface might be more practical in a real-world deployment).

**2. Project Goals**

*   **Personalized Recommendations:** Generate movie recommendations tailored to individual users.
*   **Accuracy:**  Maximize the relevance and accuracy of recommendations.
*   **Scalability:** Design the system to handle a reasonable number of users and movies.  (Note: While MATLAB is not the ideal environment for massive datasets, we'll focus on efficient algorithms within MATLAB's capabilities).
*   **Explainability:**  Provide some level of explanation for why a particular movie is recommended to a user (e.g., "Because you liked action movies starring actor X").
*   **User-Friendly (Prototype):** Create a basic, functional user interface within MATLAB (or define a clear API for integration with a front-end).

**3. Key Components and Logic**

The system will consist of the following key components:

*   **Data Acquisition & Preprocessing:**
    *   **Data Sources:**
        *   **Movie Data:**  Title, Genre(s), Director(s), Actors, Description, Year, Average Rating, potentially links to movie posters (images).
        *   **User Data:** User ID, Movie IDs watched, Ratings given (if any), Implicit preferences (e.g., watch time, number of views, whether the movie was completed).
        *   **External Data (Optional):**  Movie reviews from websites (e.g., IMDB, Rotten Tomatoes), social media sentiment.
    *   **Data Cleaning:** Handling missing values, inconsistent data formats, and errors in the data.
    *   **Data Transformation:**
        *   Converting categorical data (e.g., genres, actors) into numerical representations (e.g., one-hot encoding).
        *   Normalizing or scaling numerical data (e.g., ratings, watch time).

*   **User Preference Modeling:**

    *   **Explicit Feedback (Ratings):**
        *   **Matrix Factorization (Collaborative Filtering):**  Decompose the user-movie rating matrix into latent feature matrices for users and movies.  This helps identify underlying patterns in user preferences and movie characteristics.  Alternating Least Squares (ALS) is a common algorithm.
        *   **User-Based Collaborative Filtering:**  Find users with similar viewing histories and preferences to the target user. Recommend movies that those similar users have liked.
        *   **Item-Based Collaborative Filtering:**  Recommend movies that are similar to movies the user has already liked. Similarity can be calculated using cosine similarity on movie feature vectors.
    *   **Implicit Feedback (Viewing History):**
        *   **Association Rule Mining:** Discover relationships between movies viewed together (e.g., "Users who watched movie A also often watch movie B").
        *   **Content-Based Filtering (Profile Building):** Create a user profile based on the characteristics of the movies they have watched.  For example, if a user has watched many action and sci-fi movies, their profile will be weighted towards those genres.  The profile can be represented as a vector of genre weights, actor weights, etc.

*   **Movie Similarity Calculation:**
    *   **Content-Based Similarity:**
        *   Calculate the similarity between movies based on their attributes (genres, actors, directors, keywords from the description).  Cosine similarity is a common metric.
    *   **Collaborative Filtering Similarity (Implicit):**  Movies watched by similar users are considered similar.

*   **Recommendation Generation:**

    *   **Hybrid Approach (Recommended):** Combine collaborative filtering and content-based filtering to leverage the strengths of both.
        *   **Weighted Hybrid:**  Assign weights to different recommendation algorithms based on their performance.
        *   **Switching Hybrid:** Use different recommendation algorithms based on the user's data availability (e.g., use content-based filtering for new users with limited viewing history, and switch to collaborative filtering as more data becomes available).
    *   **Ranking:**  Rank the potential movie recommendations based on a score calculated from the preference modeling and similarity calculations.
    *   **Filtering:**  Remove movies the user has already watched from the recommendation list.

*   **Evaluation:**

    *   **Metrics:**
        *   **Precision:**  The proportion of recommended movies that the user actually likes.
        *   **Recall:**  The proportion of movies the user likes that are actually recommended.
        *   **F1-Score:**  The harmonic mean of precision and recall.
        *   **Mean Absolute Error (MAE):**  The average absolute difference between predicted ratings and actual ratings (if ratings are available).
        *   **Root Mean Squared Error (RMSE):**  The square root of the average squared difference between predicted ratings and actual ratings.
        *   **NDCG (Normalized Discounted Cumulative Gain):** Measures the ranking quality of recommendations.
    *   **Techniques:**
        *   **Hold-out Validation:**  Split the data into training and testing sets. Train the model on the training data and evaluate its performance on the testing data.
        *   **Cross-Validation:**  Divide the data into multiple folds. Train and evaluate the model multiple times, using different folds as the testing set each time.
        *   **User Studies:**  Ask users to rate the relevance of the recommendations they receive.

*   **User Interface (Prototype):**
    *   A simple MATLAB GUI (Graphical User Interface) could be created for:
        *   User login/selection
        *   Displaying recommended movies (with title, genre, description)
        *   Allowing users to rate movies
        *   Providing feedback on the recommendations.

**4. MATLAB Implementation Details**

*   **Data Handling:**
    *   `readtable` or `csvread` to read data from CSV or text files.
    *   `table` data structure to store and manipulate data.
    *   Sparse matrices for handling the user-movie rating matrix (to save memory).
*   **Algorithms:**
    *   Implement Matrix Factorization using Alternating Least Squares (ALS). MATLAB's built-in functions like `svd` (Singular Value Decomposition) can be helpful.
    *   Implement cosine similarity using `pdist2` or manually calculate it.
    *   Implement content-based filtering using vector operations and distance calculations.
*   **GUI:**
    *   MATLAB's `guide` tool can be used to create a basic GUI.  Alternatively, you could focus on building the core recommendation engine and define a clear API (input/output format) for later integration with a web-based front-end built using languages like Python/Flask or JavaScript/React.
*   **Parallel Processing (Optional):**  If you have a large dataset, consider using MATLAB's parallel processing toolbox to speed up computations.

**5. Code Structure (Example)**

```matlab
% Main Script: MovieRecommendationSystem.m

% 1. Data Loading and Preprocessing
[movieData, userData] = loadAndPreprocessData('movies.csv', 'ratings.csv');

% 2. User Preference Modeling
[userProfiles, movieLatentFeatures] = createPreferenceModels(userData, movieData);

% 3. Movie Similarity Calculation
movieSimilarityMatrix = calculateMovieSimilarity(movieData);

% 4. Recommendation Generation
userId = 1; % Example user
recommendations = generateRecommendations(userId, userProfiles, movieLatentFeatures, movieSimilarityMatrix, movieData, userData);

% 5. Display Recommendations
displayRecommendations(userId, recommendations, movieData);

% Functions (separate .m files):

% loadAndPreprocessData.m: Loads and preprocesses movie and user data.
% createPreferenceModels.m: Creates user profiles and movie latent features.
% calculateMovieSimilarity.m: Calculates movie similarity matrix.
% generateRecommendations.m: Generates movie recommendations for a user.
% displayRecommendations.m: Displays the movie recommendations to the user.
```

**6. Real-World Considerations**

*   **Scalability:** MATLAB is not ideal for extremely large datasets or high-traffic applications. For a production system, you would likely need to migrate the core algorithms to a more scalable platform like Python (with libraries like scikit-learn, TensorFlow, or PyTorch) or Java/Scala (with Spark).
*   **Data Storage:**  A real-world system would need a robust database (e.g., MySQL, PostgreSQL, MongoDB) to store movie data, user data, and recommendation data.
*   **Real-time Recommendations:**  The system should be able to generate recommendations quickly in response to user actions.  This may require caching recommendations or using optimized algorithms.
*   **Cold Start Problem:**  How to handle new users or new movies with no or limited data.  Strategies include:
    *   **Content-based filtering:**  Use movie attributes to recommend movies to new users.
    *   **Popularity-based recommendations:**  Recommend the most popular movies to new users.
    *   **Ask new users for their preferences:**  Ask users to rate a few movies when they first sign up.
*   **Diversity:**  Ensure that the recommendations are diverse and not too similar.  Techniques include:
    *   **Penalizing similar movies.**
    *   **Re-ranking recommendations to increase diversity.**
*   **Explainability:**  Provide users with explanations for why they are receiving certain recommendations.  This can increase trust and satisfaction.
*   **A/B Testing:**  Continuously test different recommendation algorithms and parameters to optimize performance.
*   **User Interface:** A web-based or mobile app is essential for a real-world system.

**7. Project Deliverables**

*   MATLAB code implementing the recommendation system.
*   A report documenting the design, implementation, and evaluation of the system.
*   A prototype user interface (optional).
*   A presentation demonstrating the system's functionality.

**8. Technologies**

*   **MATLAB:** For algorithm development and prototyping.
*   **Data Files:** CSV or text files for storing movie and user data.
*   **(Real-world Deployment):** Python, Java/Scala, Databases (MySQL, PostgreSQL, MongoDB), Web Frameworks (Flask, Django, React).

**9. Timeline (Example)**

*   **Week 1-2:** Data collection and preprocessing.
*   **Week 3-4:** Implementation of collaborative filtering algorithms.
*   **Week 5-6:** Implementation of content-based filtering.
*   **Week 7-8:** Implementation of the hybrid recommendation system.
*   **Week 9-10:** Evaluation and testing.
*   **Week 11-12:** User interface (optional) and report writing.

This detailed project outline provides a solid foundation for building your intelligent movie recommendation system in MATLAB. Remember to start with a small, manageable dataset and gradually increase the complexity of the system. Good luck!