Collecting and analyzing user interactions, feedback, and historical data
Understanding user preferences and interests
Extracting relevant features from user data and content items
Selecting and applying recommendation algorithms
Training recommendation models using historical data
Evaluating recommendation system performance using metrics
Generating personalized recommendations
Applying diversity-promoting mechanisms