Survey on Web Mining Techniques and Challenges of E-commerce in Online Social Networks

Asha, K. N.; Rajkumar, R.

doi:10.17485/ijst/2016/v9i13/85481

Cited by 3 publications

(3 citation statements)

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“…In this paper, we exploit results from existing methodologies such as visitor path analysis, 44,58,70,73,90‐93 top engagement metrics, 66 clickstream data, 68,127 content usage logging techniques, 39,72 and other approaches suitable for analyzing websites in order to increase revenue and customer satisfaction through careful analysis of visitor interaction with a website. Our study supports existing studies 55,75‐79,85‐87 that examine consumers behavioral patterns online.…”

Section: Review Of Related Literaturesupporting

confidence: 87%

“…The results show the traffic is more ordered. Similarly, Asha and Rajkumar 86 discuss web usage mining techniques for enhanced quality of experience of customers shopping online on websites and also discuss web mining techniques to find dishonest recommenders in open social networks. They propose a recommendation system that uses semantic web mining process integrated with domain ontology which can be used to extract interesting patterns from complex and heterogeneous data.…”

Section: Review Of Related Literaturementioning

confidence: 99%

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Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Ehikioya

Zeng

2021

Engineering Reports

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A successful business intelligence solution can help organizations improve the quality and speed of their decision‐making processes by analyzing the consolidated information collected from their websites. Using the current Web server log standard, which indicates only the locations of served Web pages, may lead to inaccurate business analysis for data driven and frequently updated static content Web pages. A properly defined Web content usage data warehouse that captures both dynamic and static contents of web pages provides rich data source for discovering interesting business rules among users' activities. This paper demonstrates the simplicity of data extract, transform and load procedures to import raw Web content usage log to various data models for data analysis, reporting and data mining tools. In this paper, we use two data mining techniques (expected maximization and the prefixspan algorithms) for visitor grouping and path analysis to find interesting patterns in Web content usage log data, an important component of e‐commerce Web sites traffic analysis. Visitor grouping uses data clustering to group visitors/sessions with similar selected attributes' value. Path analysis mines common visiting path sequence. The ultimate goal is to enable the online merchant provide enhanced and differentiated marketing services generally to its existing and potential customers and tell what items customers are interested in instead of ambiguous URLs.

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Section: Review Of Related Literaturesupporting

confidence: 87%

Section: Review Of Related Literaturementioning

confidence: 99%

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Ehikioya

Zeng

2021

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…The results show the traffic is more ordered. Similarly, Asha and Rajkumar [30] discuss web usage mining techniques for enhanced quality of experience of customers shopping online and also discuss web mining techniques to find dishonest recommenders in open social networks.…”

Section: Review Of Related Literaturementioning

confidence: 99%

A Traffic Tracking Analysis Model for the Effective Management of E-commerce Transactions

Ehikioya¹,

Lu²

2020

IJNDC

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Many organizations now rely on the Web to conduct business. The increasing popularity of the Internet and e-commerce makes online merchants to constantly seek tools/mechanisms that would permit them to attract new and retain old customers. To achieve these two goals, a business should know, as much as possible, about its customers. Customers are the pivot of all businesses. When businesses lack knowledge of their customers, such businesses cannot efficiently develop their marketing activities.

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A four-gram unified event model for web mining

Zou

2017

Cluster Comput

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Survey on Web Mining Techniques and Challenges of E-commerce in Online Social Networks

Cited by 3 publications

References 0 publications

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

A Traffic Tracking Analysis Model for the Effective Management of E-commerce Transactions

A four-gram unified event model for web mining

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