Multidimensional individualized recommendation method in heterogeneous network

Abstract

The invention relates to a multidimensional individualized recommendation method in a heterogeneous network which includes: 1, obtaining information; 2, establishing a similarity matrix between the user and item; 3, establishing a semi-structured heterogeneous information network; calculating the degree of correlation between users and items and between users and users under different meta paths based on the semi-structured heterogeneous information network; distributing different weights to the degree of correlation of each meta path to form a similarity matrix between users and other types of entities in the semi-structured heterogeneous information network; 4, distributing different weights and integrating the degree of correlation between users and items and between users and users with user's preference to items to form the final similarity matrix; 5, recommending a plurality of items with big similarity in the final similarity matrix to users. The multidimensional individualized recommendation method is added with user subordinate information and item subordinate information, considers the rich semantic information between users and items, and improves the recommendation accuracy and percentage of coverage.

Description

technical field [0001] The invention relates to a meta-path-based correlation search method in a heterogeneous information network and a multi-dimensional personalized recommendation method combined with user feedback information and subordinate information, and belongs to the technical field of data mining and machine learning. Background technique [0002] In recent years, heterogeneous information networks have been widely studied because they are good at representing various relationships between different types of entities and can accurately distinguish different contexts in information networks to mine more meaningful knowledge. Among them, the meta-path representing the relationship between two entities is a unique concept in the heterogeneous information network. Different meta-paths represent different physical meanings. The rich semantic features contained in the meta-path are a part of the heterogeneous information network. A very important feature. Many meta-pat...

AI Technical Summary

Problems solved by technology

Traditional collaborative filtering algorithms are divided into memory-based collaborative filtering algorithms and model-based collaborative filtering algorithms. Both of them only use the information of a single user-item matrix, and the recommendation accuracy is limited.

Facing the shortcomings of the traditional recommendation system, which has a single relationship between users and items, does not consider the content of items and user attributes, and does not consider the rich semantic information between users and items, how to combine traditional recommendation methods with user feedback information and heterogeneous information networks? It is a problem to be solved to combine the comprehensive information integration ability of the system with the rich semantic information contained in it to make personalized recommendations.

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