How do you update and maintain your content-based filtering model for movies over time?

1 Collect new data

The first step is to collect new data on the movies and the users that you want to include in your recommender system. You can use various sources, such as web scraping, APIs, or databases, to obtain information such as titles, genres, ratings, reviews, actors, directors, and so on. You should also update your user profiles with their latest ratings, preferences, and feedback. The more data you have, the better you can capture the diversity and complexity of the movie domain.

2 Extract features

The next step is to extract features from the movie data that you can use to represent the content and the similarity of each movie. You can use different methods, such as natural language processing, computer vision, or metadata analysis, to extract features such as keywords, topics, genres, themes, sentiments, images, colors, and so on. You should also normalize and standardize your features to make them comparable and consistent. The features should reflect the aspects of the movies that are relevant and important for your users.

3 Update similarity scores

The third step is to update the similarity scores between the movies based on the features that you extracted. You can use different measures, such as cosine similarity, Jaccard index, or Euclidean distance, to calculate how similar two movies are based on their feature vectors. You should also consider the weight and importance of each feature for your users. The similarity scores should capture the degree of match and relevance between the movies.

4 Evaluate performance

The final step is to evaluate the performance of your content-based filtering model and see how well it recommends movies to your users. You can use different metrics, such as precision, recall, F1-score, or mean average precision, to measure how accurate and relevant your recommendations are. You should also collect and analyze user feedback, such as ratings, reviews, likes, dislikes, and so on, to see how satisfied and engaged your users are. The performance evaluation should help you identify the strengths and weaknesses of your model and guide you for further improvement.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?