Content-based filtering in recommender systems recommends items by matching item features to a user’s preferences, derived from their interaction history. Unlike methods that rely on user behavior patterns (like collaborative filtering), content-based filtering focuses solely on the attributes of items and the user’s engagement with those attributes. For example, if a user frequently watches sci-fi movies, the system recommends other movies tagged with sci-fi elements, regardless of what other users watch. This approach requires two core components: a structured representation of item features and a user profile that reflects their preferences.
The process begins by creating item profiles, which are vector representations of item attributes. For instance, a movie might be represented by features like genre, director, actors, keywords in the plot description, or release year. Text-based features (e.g., article summaries or product descriptions) are often converted into numerical vectors using techniques like TF-IDF or word embeddings. Next, the system builds a user profile by aggregating features of items the user has interacted with. If a user rates several action movies highly, their profile will weight action-related features more heavily. Similarity metrics like cosine similarity or Euclidean distance are then used to compare the user profile against all item profiles, ranking items by how closely their features align with the user’s preferences.
Developers implementing content-based filtering must address practical considerations. Feature engineering is critical: irrelevant or poorly defined features degrade recommendations. For example, using overly broad movie genres (like “drama”) might reduce specificity, while including sub-genres (like “cyberpunk”) improves relevance. Scalability is another factor: while content-based filtering avoids the user-user computations of collaborative filtering, generating and updating user profiles in real-time can be resource-intensive. Libraries like scikit-learn simplify tasks like TF-IDF vectorization and similarity computation. A key advantage is handling cold-start scenarios for new items (if features are available), though new users with no interaction history still pose challenges. Content-based filtering works well in domains like news recommendation (matching articles to reading history) or e-commerce (suggesting products with similar attributes), where item metadata is rich and structured.