User based collaborative filtering hybrid recommendation method

Abstract

The invention relates to the computer collaborative recommendation method and discloses a user based collaborative filtering hybrid recommendation method. The user based collaborative filtering hybrid recommendation method comprises the following steps of a grade matrix establishing step, namely, establishing a user-item grade matrix, a similarity calculation step, namely, calculating the similarity between different users, a fuzzy matrix establishing step, namely, establishing a fuzzy similar matrix based on the similarity between the different users, a relation cluster construction step, namely, constructing a nearest neighbor set based on an undirected graph and a prediction step, namely, predicting user items which do not graded. The user based collaborative filtering hybrid recommendation method has the advantages of effectively solving the problem that the recommendation accuracy is low caused by incomplete user recommendation, being simple in calculation method, few in step, small in complexity and high in calculation accuracy, improving the recommendation accuracy due to prediction of the user items which do not graded and having good application values.

Description

technical field [0001] The invention relates to a computer collaborative recommendation method, in particular to a user-based collaborative filtering combination recommendation method. Background technique [0002] When faced with a large amount of software information, users often get lost in the product information space and cannot find the information they need smoothly. The personalized cloud service recommendation system integrates a large amount of user data, such as user registration information, sales ranking and user consumption history, and then simulates a store salesperson to provide product recommendations to the user to help the user find the desired product, thereby successfully completing the purchase process. [0003] In the field of recommendation system, there are many recommendation technologies based on data mining, and User-based collaborative filtering recommendation and fuzzy clustering are currently more popular recommendation methods. [0004] User...

AI Technical Summary

Problems solved by technology

[0006] The two recommendation technologies have their own scope of application. The collaborative filtering recommendation method is currently the most successful and the most in-depth researched recommendation method. The user-based collaborative filtering recommendation can handle more complex unstructured objects, but there is still extreme data sparsity. , new users, and cold starts

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