Collaborative filtering recommendation method based on nearest neighbor information
A collaborative filtering recommendation and nearest neighbor technology, applied in special data processing applications, instruments, business, etc., can solve the problems of low selection accuracy and recommendation accuracy, increase the time window, ensure consistent preferences, and increase the forgetting factor Effect
Inactive Publication Date: 2018-11-23
TIANJIN UNIV
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[0006] In order to solve the problems of low accuracy of nearest neighbor selection and low recommendation accuracy under the condition of sparse data, the present invention provides a more accurate personalized recommendation method. On the basis of traditional collaborative filtering algorithm, a collaborative filtering based on nearest neighbor information is established. recommended method
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[0022] The present invention establishes a collaborative filtering recommendation method based on the nearest neighbor information on the basis of the traditional collaborative filtering algorithm, which includes two key models: an availability calculation model and a dynamic trust calculation model.
[0023] To achieve the above object, the present invention takes the following technical solutions:
[0024] (1) Availability calculation model: The traditional similarity calculation method mainly relies on the common scoring item set, so when the number of common scoring items of two users is small, the deviation between the obtained similarity and the actual situation is large. At the same time, the traditional similarity calculation method considers that the recommendation ability between users is symmetric, but in the actual situation, the recommendation ability of two users to each other is not the same. In view of the above problems, the present invention considers the rat...
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The invention relates to a collaborative filtering recommendation method based on nearest neighbor information. The method comprises the following steps of step 1, inputting a rating data set in a user-project matrix and determining a target user u; step 2, computing availability between the target user and other users through rating data and Pearson similarity, selecting k' users with higher availability, and generating a nearest neighbor candidate set of the target user u; step 3, computing trust degree between each user v and the target user u in the same time window thetaNu'; step 4, filtering the users with higher availability and lower trust degree, selecting K users with higher trust degree as nearest neighbor users, and generating a nearest neighbor set; step 5, computing predictedscores of all non-rated projects of the target user u by utilizing rating information of Nu; and step 6, selecting a project with high predicted score to generate a recommended project list.
Description
technical field [0001] The invention belongs to the technical field of recommendation based on collaborative filtering, and in particular relates to a collaborative filtering recommendation method based on nearest neighbor information. Background technique [0002] With the rapid development of the Internet today, the network has long been integrated into human daily life. At the same time, network information has also become complicated and rapidly increasing, which makes the Internet face problems such as "information overload" when sharing information. In the field of e-commerce, the phenomenon of "information overload" is quite serious. In this context, personalized recommendation technology has gradually developed. [0003] Compared with traditional search methods, the personalized recommendation system provides users with unique services. It can collect users' historical behavior data, analyze users' interests and potential interests, provide users with products they...
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CPCG06Q30/0631
Inventor 韩玥王颖张子洋金志刚
Owner TIANJIN UNIV