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Mining Users Interest Navigation Patterns Using Improved Ant Colony Optimization


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Abstract

Web log mining is mainly to acquire users2019 interest navigation patterns from web logs and has been the subject of the web personalization research. In this paper, we define a new concept 201Cinterest pheromone201D and present a group users2019 navigation paths model. Then we propose a simple algorithm based on improved Ant Colony Optimization (ACO) to mine users2019 dynamic interest. In this algorithm, three factors relative browsing time, access frequency and operation time are considered to measure the 201Cinterest pheromone201D, which better reflects users2019 real interest. Finally, we conduct the simulation experiments to contrast the accuracy of navigation patterns mined by our approach and existing approaches. Experimental results illustrate that the proposed paradigm can truly capture users2019 browsing preference effectively.


Keywords


Pages

Total Pages: 10
Pages: 445-454

DOI
10.1080/10798587.2015.1015778


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Published

Volume: 21
Issue: 3
Year: 2015

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References

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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