A hybridized semantic trust-based framework for personalized web page recommendation

被引：5
作者：
Deepak G. [1 ]
Shwetha B.N. [1 ]
Pushpa C.N. [2 ]
Thriveni J. [2 ]
Venugopal K.R. [2 ]
机构：
[1] Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore
[2] Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore
来源：
Deepak, Gerard (gerry.deepu@gmail.com) | 1600年 / Taylor and Francis Ltd.卷 / 42期
关键词：
latent Dirichlet allocation; Latent semantic analysis; personalized web search; recommendation systems trust; web usage information;
D O I：
10.1080/1206212X.2018.1480472
中图分类号：
学科分类号：
摘要：
The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users’ queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies. © 2018 Informa UK Limited, trading as Taylor & Francis Group.
引用
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页码：729 / 739
页数：10
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