What does a knowledge-based recommendation system primarily rely on for its functionality?

A knowledge-based recommendation system primarily relies on rich metadata about items to function effectively. These systems are typically designed to recommend products or services based on a detailed understanding of the items themselves and the specific needs or preferences of users. Instead of learning patterns from user behavior as collaborative filtering methods do, knowledge-based systems leverage explicit information, such as features, specifications, and constraints related to items.

Rich metadata provides the context and attributes necessary for understanding the characteristics that make an item suitable for a particular user. For instance, in a real estate application, metadata may include property type, square footage, location, and price range, all of which help formulate effective recommendations based on user requirements.

Other approaches like examining user preferences alone might not encompass the broad range of options, while collaborative consumption data focuses more on aggregating user interactions rather than the inherent qualities of the items being recommended. Historical user logs provide insights into past behaviors but do not capture the detailed attributes of items needed for a robust knowledge-based recommendation. Therefore, the reliance on rich metadata is crucial for tailoring recommendations accurately in a knowledge-based system.