A personalized information recommendation method based on metapath with attributes

Abstract

The invention discloses a personalized information recommendation method based on metapath with attributes, including modeling all the information as a heterogeneous information network, dividing themetapath with attributes into completely symmetrical and semi-symmetrical metapath with attributes according to whether the attribute values are the same, obtaining the correlation matrix of entitiesunder each metapath with attributes, and obtaining the correlation matrix of entities in the whole network by weighting; based on the matrix decomposition algorithm combined with the regular term which is composed of the correlation degree and the correlation degree weight vector, the implicit meaning matrix of the user and the object, constructing the objective function and updating the implicitmeaning matrix and the correlation degree weight vector iteratively, and calculating the prediction score of the user to the object from the obtained implicit meaning matrix, according to the predicted score, recommending the item to the user as the object to be recommended. The invention satisfies the personalized demand of the user for the information recommendation, can improve the recommendation accuracy and solve the cold start problem to a certain extent.

Description

technical field [0001] The invention relates to a method for recommending personalized information based on an attribute-bearing meta-path, which belongs to the technical field of the Internet. Background technique [0002] With the rapid development of Internet technology and the rapid growth of information, the problem of information overload is becoming more and more serious. Many commercial companies use recommendation algorithms extensively in the system to improve user satisfaction, increase user stay time and consumption, and increase company revenue. Now, large-scale commercial systems often contain a large amount of heterogeneous information; for example, in Taobao, products have attributes including categories, brands, and materials, and users also have information including phone numbers and permanent addresses. If the system can abstract these diverse and semantically rich information into heterogeneous information networks (Heterogeneous Information Networks, H...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide a personalized information recommendation method based on meta-paths with attributes, and solve the problem of many existing correlation regularization information recommendation methods based on matrix decomposition algorithms. The information utilization in HIN is not complete, and the semantic information contained in the asymmetric meta-path with attributes is often ignored, resulting in the problem that the accuracy of the recommendation results is not high enough.

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