Electronic computing device and method for recommending personalized information

Abstract

An electronic computing device and its method for providing personalized information. The electronic computing device is used to create a first tree data and a first tree data and a first tree data by using an ontology tree construction algorithm to arrange a first data of a first user and a second data of a second user arranged in a period of time. Two tree-like data, and calculate a similarity between the first tree-like data and the second tree-like data with a similarity evaluation algorithm, and use a grouping algorithm to divide the first tree-like data and the second tree-like data according to the similarity The tree data is summarized into a group, and a first difference information of the first tree data relative to the second tree data is determined according to the group, and a time period corresponding to the first user is generated according to the first difference information A recommended information, and enable a screen to display the recommended information.

Description

technical field [0001] The invention relates to an electronic computing device and a method for recommending personalized information thereof. More specifically, the electronic computing device of the present invention establishes the user's personal behavior data as a tree structure, and compares each other to generate a similarity, and then analyzes the similarity with a grouping algorithm to obtain the user's personal data. Set up groups, and then use the group with the highest relevance to recommend information within the group. Background technique [0002] In the modern age of advancement in information technology, users have many ways to obtain information, one of which is that the processing system recommends information to users through data correlation. As far as the current recommendation method is concerned, a matrix data is usually used to evaluate the user's behavior data, and then use the evaluation result to recommend information for the user. [0003] Spec...

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Problems solved by technology

[0004] However, the above recommendation method cannot calculate the information correlation between consumers when there are more than two consumers, which leads to mutual recommendation between consumers who buy commodities with different attributes, and greatly reduces the effectiveness of recommended information.

Moreover, the information obtained by the above-mentioned recommendation method is only the cumulative result of consumer data, and it has not been effectively separated in time and evaluated at the same time. If the life of consumers changes significantly, the above-mentioned recommendation method cannot analyze Circumstances that should change substantially

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