The most common types of recommender systems in e-commerce are collaborative filtering, content-based filtering, and hybrid systems. Each approach addresses different aspects of user behavior and item characteristics to generate personalized recommendations. Developers often combine these methods or optimize them based on data availability and business goals.
Collaborative filtering analyzes user-item interactions to identify patterns. For example, if User A and User B have similar purchase histories, items liked by User A but not yet seen by User B might be recommended. This method relies on matrix factorization or neighborhood-based techniques (e.g., k-nearest neighbors) to predict preferences. A classic example is Amazon’s “Customers who bought this also bought” feature, which leverages historical purchase data. However, collaborative filtering struggles with cold-start problems (e.g., new users or items with limited interaction data) and scalability issues in large datasets.
Content-based filtering focuses on item attributes rather than user behavior. It recommends items similar to those a user has previously liked, based on metadata like product descriptions, categories, or tags. For instance, if a user frequently buys running shoes, the system might recommend other athletic footwear by comparing product features. Netflix’s genre-based recommendations (e.g., suggesting sci-fi movies to a sci-fi viewer) are a well-known example. This approach avoids cold-start issues for new items but can lead to overspecialization, limiting discovery of diverse products. Developers often use techniques like TF-IDF or word embeddings to quantify item similarity.
Hybrid systems combine collaborative and content-based methods to mitigate individual weaknesses. For example, Spotify uses collaborative filtering to recommend playlists based on user listening habits while incorporating audio features (e.g., tempo, genre) for song-level suggestions. Hybrid models might use weighted averages, ensemble learning, or layered architectures. Many e-commerce platforms adopt hybrid approaches—like using collaborative filtering for broad trends and content-based filtering for niche items—to balance personalization and diversity. Frameworks like TensorFlow Recommenders or libraries like Surprise simplify implementation, letting developers experiment with hybrid configurations efficiently.