Clustering algorithm and local sensing reconstruction model-based author recommendation method and system

Abstract

The invention discloses a clustering algorithm and local sensing reconstruction model-based author recommendation method and system. According to the method and system, author information expressed bya tree structure is converted to be expressed by a uniform vector through tree structure expression of author related information, feature expression of nodes, position mapping of hierarchical nodesand establishment of a local sensing reconstruction model; the vector comprises the author related information and structure information of author related layers; and furthermore, related author recommendation and retrieval is carried out according to uniform vector expression of author information. The method comprises the following steps of: A, tree structure expression; B, node feature expression; C, hierarchical node position mapping; D, local sensing reconstruction model establishment and solution; E, uniform vector expression of a tree structure; and F, content-based author recommendation and retrieval.

Description

technical field [0001] The present invention belongs to the field of text mining and recommendation systems, organizes heterogeneous information data into a tree structure according to its internal logic structure, and realizes an effective tree structure vector representation through clustering at each level and local perceptual reconstruction models. The method and system use the most original input of author information from different domains. Background technique [0002] With the continuous advancement and development of Internet technology, the scale of network data is increasing day by day. The source of data and the organizational form of data are diverse according to different application scenarios. For each user, the data sources related to it are diverse. If these data can be effectively organized, extracted, and fused, a more comprehensive information representation related to the user can be finally obtained. For example, for a certain author, data from differ...

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Problems solved by technology

However, this type of method only relies on the word frequency statistics of words in the text, and ignores the spatial distribution information of words, which makes it difficult for this method to distinguish two samples with similar word frequencies but different spatial distributions of words.

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