Method for collaborative filtering recommendation based on item level types

Abstract

The invention provides a method for collaborative filtering recommendation based on item level types. The method for collaborative filtering recommendation based on the item level types comprises the four steps of preference description, item type grading, similarity calculation and predictive grading. In the step of preference description, modeling of preference of a user is completed and a user-item grading matrix is generated. In the step of item type grading, grades, offered by a user, of bought items are defined, the similarity between the item types is defined according to the association rules, and grades, offered by the user, of item types which are not bought are deduced. In the step of similarity calculation, the similarity between every two items of a system is obtained according to the similarity calculation formula. In the step of predictive grading, grades, offered by the user, of the items which are not graded are predicated. According to the method for collaborative filtering recommendation based on the item level types, the number of item level type factors is increased, the new similarity formula is established, the influence of sparseness of the user-item grading matrix on the accuracy of the item similarity is reduced, the probability of the phenomenon that the similarity between every two items is zero is reduced, and the accuracy of recommendation is remarkably improved. The method for collaborative filtering recommendation based on the item level types can be applied to the fields such as the field of data miming and the field of recommendation systems.

Description

technical field [0001] The invention proposes a collaborative filtering recommendation method based on item hierarchical categories, which belongs to the technical field of computer data mining recommendation. Background technique [0002] With the development of Web2.0, e-commerce websites are more focused on user participation and user contribution. As users frequently visit Web sites, the system usually generates a large amount of user data, which records user behavior. The recommendation method based on these user behaviors is an important method of a personalized recommendation system, and the academic circle generally refers to this type of method as a collaborative filtering recommendation method. [0003] The principle of the collaborative filtering method is to discover the correlation between users or between items according to the user's preference for items, and then make recommendations based on these correlations. It can be seen that the core function of a re...

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None of the above-mentioned patents involves or discloses the collaborative filtering scoring recommendation technology based on item category

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