The application discloses a user
cold start recommendation
system and method based on
similarity matching, constructs a
knowledge graph database according to users, products and the interaction relationship between the users and the products, and carries out
standardization processing. The processed data is extracted to
score information and mapped to a user like-dislike type space, so that the like-dislike
product type of each user is determined, and a
similarity matching algorithm is designed to find the old user with the highest similarity to the new user. The old user
product type score vector and the new user
product type vector
score are processed by an association function and used as the recommendation basis of the
system, the reasonable expansion of the new user data is completed, and the
cold start problem of the recommendation
system is effectively alleviated. The application solves the user
cold start problem caused by the introduction of a new user into an industrial recommendation system. The application can be used for the scene of effectively recommending the product types that meet the preferences of new users in the industrial recommendation system.