Automatic Recommendation of Web Pages for Online Users Using Web Usage Mining

2012 ◽  
Author(s):  
Ravi Bhushan ◽  
Rajender Nath
Keyword(s):  
Web Usage Mining ◽  
Web Pages ◽  
Web Usage

Effectiveness of Web Usage Mining Techniques in Business Application

2017 ◽  
pp. 324-350 ◽  
Author(s):  
Ahmed El Azab ◽  
Mahmood A. Mahmood ◽  
Abd El-Aziz
Keyword(s):  
Data Mining ◽  
Academic Research ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Data ◽  
Web Data Mining ◽  
Web Usage ◽  
Business Application ◽  
Common Interests ◽  
The Web

Web usage mining techniques and applications across industries is still exploratory and, despite an increase in academic research, there are challenge of analyze web which quantitatively capture web users' common interests and characterize their underlying tasks. This chapter addresses the problem of how to support web usage mining techniques and applications across industries by combining language of web pages and algorithms that used in web data mining. Existing research in web usage mining techniques tend to focus on finding out how each techniques can apply in different industries fields. However, there is little evidence that researchers have approached the issue of web usage mining across industries. Consequently, the aim of this chapter is to provide an overview of how the web usage mining techniques and applications across industries can be supported.

Web Usage Mining through Associative Models

2011 ◽  
pp. 1231-1234 ◽  
Author(s):  
Paolo Giudici ◽  
Paola Cerchiello
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Page ◽  
User Session ◽  
Web Usage ◽  
Associative Models ◽  
The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

Web Recommendation Based on Unified Collaborative Filtering

2011 ◽  
Vol 219-220 ◽  
pp. 887-891
Author(s):  
Jiang Zhong ◽  
Yi Feng Cheng ◽  
Shi Tao Deng
Keyword(s):  
Collaborative Filtering ◽  
Semantic Analysis ◽  
Back Propagation ◽  
Web Usage Mining ◽  
Probabilistic Latent Semantic Analysis ◽  
Web Pages ◽  
Web Content ◽  
Time Prediction ◽  
Web Usage ◽  
Latent Features

Web usage mining technique is widely used for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining can only discover usage pattern explicitly. In order to employ the users’ feature and web pages’ attributes to get more accuracy recommendation, we propose a unified collaborative filtering model for web recommendation which combined the latent and external features of users and web page through back propagation neural networks. In the algorithm, we employ Probabilistic Latent Semantic Analysis (PLSA) method to get latent features. The main advantages of this technique over standard memory-based methods are the higher accuracy, constant time prediction, and an explicit and compact model representation. The preliminary experimental evaluation shows that substantial improvements in accuracy over existing methods can be obtained.

Rule Optimization of Web-Logs Data Using Evolutionary Technique

2014 ◽  
pp. 180-192
Author(s):  
Manish Kumar ◽  
Sumit Kumar
Keyword(s):  
Web Server ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Usage ◽  
Data Application ◽  
Level Data ◽  
Typical Data ◽  
Evolutionary Technique ◽  
User Access ◽  
Access Patterns

Web usage mining can extract useful information from Weblogs to discover user access patterns of Web pages. Web usage mining itself can be classified further depending on the kind of usage data. This may consider Web server data, application server data, or application level data. Web server data corresponds to the user logs that are collected at Web servers. Some of the typical data collected at Web server are the URL requested, the IP address from which the request originated, and timestamp. Weblog data is required to be cleaned, condensed, and transformed in order to retrieve and analyze significant and useful information. This chapter analyzes access frequent patterns by applying the FP-growth algorithm, which is further optimized by using Genetic Algorithm (GA) and fuzzy logic.

scholarly journals Optimal Biclustering using Hybrid Swarm Intelligence for Web usage Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 6392-6395
Keyword(s):  
Swarm Intelligence ◽  
Web Usage Mining ◽  
Web Pages ◽  
Crossover Operator ◽  
Critical Research ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Clustering Techniques ◽  
Web Usage ◽  
Browsing Behavior

Web usage mining is used to analyze the user browsing behavior among the web pages which can be further utilized in other applications like recommender system, personalized web pages, providing insight for better business functionality. Since this type of mining does not only depends on the user or web pages, conventional clustering techniques may not suit very well for the analysis. Biclustering techniques are used to discover the subset in the form of submatrices as objects and attributes of objects are considered symmetrically. Finding optimal biclusters is a critical research issue. This research proposes a hybrid swarm intelligence-based method having Particle Swarm Optimization combined with Leader Clustering method along with Uniform Crossover operator. The experimental study shows that the proposed method performs well than traditional biclustering techniques in terms of evaluation metrics.

Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods

2018 ◽  
Vol 17 (06) ◽  
pp. 1743-1776 ◽  
Author(s):  
Jozef Kapusta ◽  
Michal Munk ◽  
Martin Drlik
Keyword(s):  
Web Mining ◽  
Web Usage Mining ◽  
Point Of View ◽  
Web Pages ◽  
Web Page ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Mining Methods ◽  
The Web

The different web mining methods and techniques can help to solve some typical issues of the contemporary websites, contribute to more effective personalization, improve a website structure and reorganize its web pages. However, only several papers tried to combine web structure and web usage mining (WUM) methods with this aim. The paper researches if and how the combination of selected web structure and WUM methods can identify misplaced web pages and how they can contribute to improving the website structure. The paper analyzes the relationship between the estimated importance of the web page from the web page creator’s point of view using the web structure mining method based on PageRank and visitors’ real perception of the importance of that individual web page using the WUM method based on sequence patterns analysis, which eliminates the problem with repeated visits of the same web page during one session. The results prove that the expected probability of accesses to the individual web page correlates with the observed visit rate obtained from the log files using the WUM method. Furthermore, the website can be improved based on the consequent application of the residual analysis on the obtained results. The applicability of the proposed combination of the web structure and WUM methods is presented on two case studies from different application domains of the contemporary web. As a result, the web pages, which are underestimated or overestimated by the web page creators, are successfully identified in both cases.

Web Usage Mining

2017 ◽  
pp. 107-130
Author(s):  
Sathiyamoorthi V
Keyword(s):  
Web Usage Mining ◽  
Internet Technology ◽  
Web Pages ◽  
Bandwidth Utilization ◽  
Proxy Caching ◽  
Web Based ◽  
Web Performance ◽  
Network Bandwidth ◽  
Web Usage ◽  
Web Objects

In recent days, Internet technology has provided a lot of services for sharing and distributing information across the world. Among all the services, World Wide Web (WWW) plays a significant role. The slow retrieval of Web pages may lessen the interest of users from accessing them. To deal with this problem, Web caching and Web pre-fetching are the two techniques used. Web proxy caching plays a key role in improving Web performance by keeping Web objects that are likely to be used in the near future in the proxy server which is closer to the end user. It helps in reducing user perceived latency, network bandwidth utilization, and alleviating loads on the Web servers. Thus, it improves the efficiency and scalability of Web based system. This chapter gives an overview of Web usage mining and its application on Web and discusses various approaches for improving the performance of Web.

Rule Optimization of Web-Logs Data Using Evolutionary Technique

2015 ◽  
pp. 158-169
Author(s):  
Manish Kumar ◽  
Sumit Kumar
Keyword(s):  
Web Server ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Usage ◽  
Data Application ◽  
Level Data ◽  
Typical Data ◽  
Evolutionary Technique ◽  
User Access ◽  
Access Patterns

Web usage mining can extract useful information from Weblogs to discover user access patterns of Web pages. Web usage mining itself can be classified further depending on the kind of usage data. This may consider Web server data, application server data, or application level data. Web server data corresponds to the user logs that are collected at Web servers. Some of the typical data collected at Web server are the URL requested, the IP address from which the request originated, and timestamp. Weblog data is required to be cleaned, condensed, and transformed in order to retrieve and analyze significant and useful information. This chapter analyzes access frequent patterns by applying the FP-growth algorithm, which is further optimized by using Genetic Algorithm (GA) and fuzzy logic.

scholarly journals Recommendation on semantic web pages based on conceptual prediction model

2018 ◽  
Vol 7 (1.7) ◽  
pp. 199
Author(s):  
Blessy Jenila R ◽  
Bharathi S
Keyword(s):  
Semantic Web ◽  
Prediction Model ◽  
Web Usage Mining ◽  
Web Pages ◽  
Semantic System ◽  
Novel Technique ◽  
Web Usage ◽  
New Models ◽  
Web Use ◽  
The Web

The development of trhe web has made a major test for guiding the client to the pages in their regions.Useful knowledge disclosure from web use information and acceptable learning portrayal for successful page suggestion are urgent and testing.In this paper we propose a novel technique to effectively give a better site page proposal through semantic upgrade by coordinating the space and web use learning of a site.Two new models are proposed to the learning.Semantic system is used to the web pages and the relations between the pages.Conceptional model produces a semantic system for web use information,which is the combination of learning and web use information.Various inquires have been created to inquiry about these learning base.Based on these questions ,an arrangement of suggestion methodologies have been proposed to produce fitting site page proposals to the client.The suggestion comes about have been contrasted and the outcomes got from a progressed existing Web Usage Mining(WUM)strategy.The exploratory outcomes show that the proposed technique delivers essentialy higher execution than the WUM technique.

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