+---------------------------+
| User Interface |
+---------------------------+
|
v
+---------------------------+
| Recommendation Engine |
+---------------------------+
|
v
+---------------------------------------------+
| User Data + Movie Data + ... |
+---------------------------------------------+
|
v
+-------------------+
| Preprocessing |
+-------------------+
|
v
+-----------------------+
| Feature Extraction |
+-----------------------+
|
v
+----------------------------+
| Machine Learning Model |
+----------------------------+
|
v
+-------------------+
| Predictions |
+-------------------+
|
v
+---------------------+
| Recommendation |
+---------------------+
User Interface: This is where users interact with the recommender system, providing input such as ratings, reviews, or preferences.
Recommendation Engine: The core of the system, responsible for generating personalized recommendations for users based on their input and historical data.
User Data & Movie Data: The raw data sources containing information about users (e.g., demographics, past interactions) and movies (e.g., genres, ratings).
Preprocessing: Cleaning, filtering, and transforming the raw data to prepare it for feature extraction and model training.
Feature Extraction: Extracting relevant features from the preprocessed data to represent users, movies, and their interactions in a meaningful way for the machine learning model.
Machine Learning Model: Trained model(s) that utilize the extracted features to make predictions, such as user preferences for movies.
Predictions: Using the trained model to predict user preferences for movies or generate recommendations.
Recommendation: The final list of recommended movies presented to the user based on the predictions made by the model.
Note: This diagram provides a high-level overview of the components and flow of a typical movie recommender system. Actual implementations may vary in complexity and architecture.