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Recuperating Eminence of Web Recommendations using Content Semantics

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International Conference and Workshop on Emerging Trends in Technology
© 2011 by IJCA Journal
Number 9 - Article 4
Year of Publication: 2011
Authors:
Raj Gaurang Tiwari
Mohd. Husain
Vishal Srivastava
Anil Agrawal

Raj Gaurang Tiwari, Mohd. Husain, Vishal Srivastava and Anil Agrawal. Recuperating Eminence of Web Recommendations Using Content Semantics. IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (9):19-26, 2011. Full text available. BibTeX

@article{key:article,
	author = {Raj Gaurang Tiwari and Mohd. Husain and Vishal Srivastava and Anil Agrawal},
	title = {Recuperating Eminence of Web Recommendations Using Content Semantics},
	journal = {IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET)},
	year = {2011},
	number = {9},
	pages = {19-26},
	note = {Full text available}
}

Abstract

The impact of the World Wide Web as a main source of information acquisition is increasing spectacularly. The existence of such abundance of information, in combination with the dynamic and assorted nature of the web, makes web site exploration a difficult process for the average user. To address the requirement of effective web navigation, web sites provide personalized recommendations to the end users. Most of the research efforts in web personalization correspond to the evolution of extensive research in web usage mining, i.e. the exploitation of the navigational patterns of the web site’s visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. Moreover, the structural properties of the web site are often disregarded.

In this paper, we propose novel techniques that use the content semantics and the structural properties of a web site in order to improve the effectiveness of web personalization. Here we present SWP (Semantic Web Personalization), a personalization system that integrates usage data with content semantics, expressed in ontology terms, in order to compute semantically enhanced navigational patterns and effectively generate useful recommendations.

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