Content-Based Filtering

Content-based filtering is a recommendation approach that suggests items to a user based on the attributes of items they have previously engaged with, rather than the behavior of other users. A content-based system analyzes item features — such as product category, brand, material, price range, or textual description — and identifies other items with similar feature profiles to those the target user has viewed, purchased, or rated positively. It does not require data from other users, making it effective for users with established interaction history and for recommending niche items with few historical interactions.

In commerce recommendation engines, content-based filtering is typically used alongside collaborative filtering in hybrid architectures. Its strengths lie in explainability (recommendations can be attributed to specific shared attributes) and cold-start resilience for items (new products can be recommended immediately based on their catalog attributes, before accumulating behavioral data). Its limitation is a tendency toward narrow recommendations — serving more of what users already know rather than surfacing genuinely novel or serendipitous discoveries. Effective commerce recommendation systems balance content-based approaches with signals from broader user populations to achieve both relevance and discovery.