Knowledge-based recommenders

Knowledge-based recommenders are used for items that are very rarely bought. It is simply impossible to recommend such items based on past purchasing activity or by building a user profile. Take real estate, for instance. Real estate is usually a once-in-a-lifetime purchase for a family. It is not possible to have a history of real estate purchases for existing users to leverage into a collaborative filter, nor is it always feasible to ask a user their real estate purchase history.

In such cases, you build a system that asks for certain specifics and preferences and then provides recommendations that satisfy those aforementioned conditions. In the real estate example, for instance, you could ask the user about their requirements for a house, such as its locality, their budget, the number of rooms, and the number of storeys, and so on. Based on this information, you can then recommend properties that will satisfy all of the above conditions.

Knowledge-based recommenders also suffer from the problem of low novelty, however. Users know full-well what to expect from the results and are seldom taken by surprise.