Content-Based Filtering

Content-based filtering recommends items by matching their attributes against a user's demonstrated preferences. If a user frequently reads articles about machine learning, the system recommends other articles tagged with AI and ML topics. The approach builds a profile of user interests from their interaction history and matches it against item feature vectors.

For growth teams, content-based filtering solves the cold-start problem for new items because recommendations depend on item attributes rather than accumulated user interactions. A newly published article or product can be recommended immediately if its features match user preference profiles. AI enhances content-based filtering through natural language processing that extracts nuanced features from text content, computer vision that understands image attributes, and embedding models that capture semantic similarity beyond keyword matching. Growth engineers should invest in rich item metadata and feature engineering because the quality of content-based recommendations is directly limited by the richness of item descriptions. The main limitation is the filter bubble effect, where users only see items similar to what they have already consumed, reducing serendipity and discovery. Combining content-based with collaborative approaches mitigates this limitation.