Content-based filtering handles item features by analyzing the attributes of items to recommend similar ones based on a user’s preferences. This approach relies on extracting descriptive features from items, such as genre, keywords, or metadata, and using these features to build a profile of what the user likes. For example, in a movie recommendation system, features might include genres (action, comedy), directors, actors, or plot keywords. Each item is represented as a vector of these features, often weighted by importance (e.g., using TF-IDF for text-based features). The system then compares these feature vectors to find items that match the user’s historical preferences.
To match user preferences, content-based filtering creates a user profile based on their interaction history. If a user frequently watches action movies starring a specific actor, the system assigns higher weights to those features. When recommending new items, it calculates similarity scores between the user’s feature vector and all item vectors. For instance, cosine similarity might measure the angle between vectors to determine how closely an item aligns with the user’s preferences. This method ensures recommendations are tailored to the user’s explicit interests. For example, if a user consistently reads tech articles tagged with “AI” and “machine learning,” the system prioritizes articles with those tags, even if they’re from new publishers the user hasn’t interacted with yet.
However, content-based filtering has limitations tied to feature handling. First, it requires rich, accurate item metadata. If features are incomplete or poorly defined (e.g., a movie lacking genre tags), recommendations suffer. Second, it can lead to over-specialization, where users only see items too similar to their past choices. For example, a user who listens to rock music might miss out on recommended jazz tracks that share thematic features but aren’t explicitly labeled as “rock.” To address this, developers often combine content-based filtering with collaborative filtering (hybrid systems) or incorporate diversity-enhancing techniques, such as clustering features to broaden recommendations. Maintaining feature quality—like updating tags or adding new attributes—is also critical to keep the system effective as item catalogs evolve.