Web User Interest Mining Based on Ontology and Patterns

SU Xue-yang; ZUO Wan-li; WANG Jun-hua

doi:10.3969/j.issn.0372-2112.2014.08.015

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Web User Interest Mining Based on Ontology and Patterns

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- Web User Interest Mining Based on Ontology and Patterns
- Acta Electronica Sinica Vol. 42, Issue 8, Pages: 1556-1563(2014)
- 作者机构：
  
  1. 吉林大学计算机科学与技术学院,吉林,长春,130012
  2. 符号计算与知识工程教育部重点实验室(吉林大学),吉林,长春,130012
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.60973040);Youth Fund of National Natural Science Foundation of China (No.61300148);Key Technology Research and Development Program of Jilin Province (No.20130206051GX);Youth Foundation of Science and Technology development Plan of Jilin Province (No.20130522112JH)
- DOI：10.3969/j.issn.0372-2112.2014.08.015
  CLC： TP393
- Published：2014
- 稿件说明：
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SU Xue-yang, ZUO Wan-li, WANG Jun-hua. Web User Interest Mining Based on Ontology and Patterns[J]. Acta Electronica Sinica, 2014, 42(8): 1556-1563.
DOI：

SU Xue-yang, ZUO Wan-li, WANG Jun-hua. Web User Interest Mining Based on Ontology and Patterns[J]. Acta Electronica Sinica, 2014, 42(8): 1556-1563. DOI： 10.3969/j.issn.0372-2112.2014.08.015.

摘要

本文探讨了用户兴趣挖掘的新方法，首先从用户搜索日志中获取访问行为元素，并借助通用本体中的概念描述网页所体现的用户个体兴趣，然后提出了一种兴趣得分计算方法，并在此基础上从用户个体兴趣序列中识别不同的兴趣模式，判断用户的短期兴趣，并利用通用本体得出用户兴趣的集合表示，最后根据短期兴趣的增量积累推算长期兴趣.整个过程避开了以往兴趣挖掘方法中通过相似度计算和文档聚类算法进行兴趣合并的问题，为兴趣发现提供了新思路.实验结果表明，本文的方法对用户兴趣的描述更具体，取得了更优化的兴趣合并结果.

Abstract

A novel user interest mining method is proposed.Firstly

the items of visiting behaviors are retrieved from user's search engine log

and individual user interests with every webpage are described through the concepts of common ontology.Then

a method for computing the score of interest is proposed.According to the scores

a user's interest list can be judged as different interest patterns

which can be used to find the user's short term interests.After that

a user's interest model is built with concept collection extracted from ontology.At last

based on incremental accumulation of short term interests

long term interest collection can be calculated.The whole procedure avoids the problem of using similarity computation and document clustering to merge concepts in existing interest mining methods.This paper explores a new way of thinking.And as the experiment shows

the proposed method provides a more concrete description of user interest model and obtains an optimized concepts merging result.

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