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A New Improved Clustering Algorithm based Diversified Web Page Recommendation

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Meghna Guru, S. Anitha Angayarkanni, J.C. Kavitha
10.5120/ijca2016909542

Meghna Guru, Anitha S Angayarkanni and J C Kavitha. Article: A New Improved Clustering Algorithm based Diversified Web Page Recommendation. International Journal of Computer Applications 140(13):17-22, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Meghna Guru and S. Anitha Angayarkanni and J.C. Kavitha},
	title = {Article: A New Improved Clustering Algorithm based Diversified Web Page Recommendation},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {140},
	number = {13},
	pages = {17-22},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

The tremendous growth of internet over the years, has given rise to the large number of web services, containing lot of information. Due to this information overload, it has become difficult to get the correct information. Web Service Recommendation system focuses on satisfying the user’s potential interests. Most of the existing recommendation approaches focus only on missing QoS values only, assuming that the result contains independent web services, which might not be true. As a result redundant web services appear in the list. The existing system takes into consideration active user’s QoS preferences as well as diversification of the web services list. First, the active user’s usage history is mined, and then the experiences of other service users are collected through collaborative filtering approach. Scores are computed for the web service candidates by measuring their relevance with historical and potential user interest and the QoS utility. Web Service graph is constructed based on the functional similarity of the web service candidates. Finally, the diversity-aware web service ranking algorithm is applied on the web service candidates based on the scores calculated and the diversified degree derived from the web service graph.

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Keywords

Web service recommendation, diversity, user interest, potential interest, QoS preference