A Guide to Content-Based Filtering In Recommender Systems

Content-based filtering uses item features to suggest additional products that are similar to what users already like by leveraging their past behavior or explicit feedback. It employs machine learning algorithms to group similar items together based on their intrinsic features.

Content-based filtering is generally used in recommender systems designed for companies offering various products, services, or content.

In the hybrid approach, both collaborative and content-based techniques are used to make recommendations. This helps the recommender engine to get the best of both worlds. Using the methods separately leads to several limitations like the cold start problem, lack of diversity in suggestions, etc. With a hybrid approach, these impediments are easily avoided and more accurate recommendations are possible.

Content-based recommender engines can operate using two methods. One employs a classification model while the other makes use of the vector spacing method. The classification approach uses machine learning models like decision trees, whereas the vector spacing method uses the distance between the user and item vectors to make suggestions.