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Personalized recommendation method and system based on interactive data clustering

A technology for interactive data and recommendation methods, which is applied in the fields of electrical digital data processing, special data processing applications, digital data information retrieval, etc., can solve problems such as unsatisfactory results, and achieve the effect of improving performance and improving accuracy

Active Publication Date: 2019-08-23
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

At the same time, when dealing with large-scale data, the effect is still not ideal

Method used

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  • Personalized recommendation method and system based on interactive data clustering
  • Personalized recommendation method and system based on interactive data clustering
  • Personalized recommendation method and system based on interactive data clustering

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[0030] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0031]A personalized recommendation method based on interactive data clustering, including clustering and classification learning. The essence of clustering is an unsupervised classification. Clustering can be described as a multidimensional space containing relatively high-density point sets. Connected regions, which are separated from other regions by regions containing relatively low-density point sets. The process of clustering is to find closely related things a...

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Abstract

The invention discloses a personalized recommendation method and system based on interactive data clustering. The method comprises the following steps: constructing a user project interaction characteristic matrix, constructing a user historical behavior matrix, classifying the interaction matrix P by using a classifier, clustering similar users, selecting adjacent projects, training a neural network, and carrying out personalized recommendation. According to the invention, historical information such as browsing records and user search records of a user to a project and user personal information are spliced to construct user characteristics; the user characteristics are clustered by using a clustering algorithm, and personalized recommendation is performed on specific users on the basis,so that the performance of a traditional matrix decomposition model is improved, matrix decomposition is combined with a multi-layer perceptron, and the relationship between user projects is learned and predicted. And the recommendation precision in a big data environment is improved.

Description

technical field [0001] The invention relates to a nonlinear network that combines matrix decomposition and a multi-layer perceptron to perform personalized recommendation and prediction for a specific user, and belongs to the field of machine learning. Background technique [0002] With the continuous development of technologies such as big data and cloud computing, recommendation systems, as an important topic in the field of service computing, have attracted much attention and are widely used in online service fields such as e-commerce, network news, and social media. The key to a personalized recommendation system is to mine items that people may be interested in based on the content of user history interactions (such as ratings, clicks, etc.), and provide users with information, goods, and services that meet their needs. [0003] At present, many literatures have conducted in-depth research on recommender systems. The recommendation technologies used in traditional reco...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06K9/62G06N3/04
CPCG06F16/9535G06N3/045G06F18/2321G06F18/214
Inventor 刘尚东李可季一木朱林超刘艳兰刘强许正阳尧海昌李奎
Owner NANJING UNIV OF POSTS & TELECOMM
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