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Method for collaborative filtering recommendation based on item level types

A collaborative filtering recommendation and project technology, applied in special data processing applications, instruments, businesses, etc., can solve the problem of not involving or public collaborative filtering scoring recommendation technology, and achieve the effect of improving recommendation accuracy and accuracy.

Active Publication Date: 2014-01-15
江苏谐云智能科技有限公司
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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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  • Method for collaborative filtering recommendation based on item level types
  • Method for collaborative filtering recommendation based on item level types
  • Method for collaborative filtering recommendation based on item level types

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Embodiment Construction

[0040] The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0041] like figure 1 As shown, the collaborative filtering recommendation method based on item hierarchical categories implemented by the present invention consists of four processing steps, namely preference description 101, item category scoring 102, similarity calculation 103, and prediction scoring 104 processing steps.

[0042] 1) Preference Description Step 101: Map explicit or implicit user historical behaviors to specific user-item ratings. Explicit ratings are beneficial for the system to process, and implicit user preferences can also be obtained by using appropriate scoring formulas.

[0043] 2) Step 102 of item category score calculation: traverse the user historical behavior database, and use association rules to deduce the user's preference for each item category. It also includes 4 processing modules: the known item category scoring modu...

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9535G06Q30/0629
Inventor 唐震陈立全朱瑶
Owner 江苏谐云智能科技有限公司
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