What is content-based filtering?

Content-based filtering recommends items similar to ones a person already likes, by comparing item attributes such as category, brand, color, style or description.

Content-based filtering looks at the items themselves. If a shopper buys relaxed-fit jeans in a dark wash, it recommends other items with similar attributes. It works for a new product as soon as its attributes are filled in, which makes it useful when there is little purchase history.

Its weakness is that it tends to recommend more of the same, and it is only as good as the product data behind it. Incomplete or inconsistent attributes lead directly to weak recommendations. Most systems combine it with collaborative filtering.