A content-based recommender suggests items whose attributes resemble products a user has previously viewed, liked, or purchased. It can match features such as color, brand, style, or price, rather than relying on behavior from similar users. section 4.4.1
Matching product characteristics
A shopper who has viewed or bought several pairs of black boots might receive recommendations for other black or similarly styled boots, including products from unfamiliar brands. section 4.4.1
This approach can shorten time-to-value for new users: a first interaction can provide enough information for an initial recommendation, even without a detailed history. It can also operate alongside collaborative filtering, combining product attributes with patterns found in other users’ behavior. section 4.4.1 section 4.5
Effective recommendation experiences require continuous refinement through metrics, testing, behavioral data, and user feedback. Context can extend the approach by considering when and where someone browses, such as prioritizing seasonal products or mobile-friendly quick buys. section 4.5 section 4.4.2
