Link Recommendation Method Based on Web Content and Usage Mining

Kazienko, Przemysław; Kiewra, Maciej

doi:10.1007/978-3-540-36562-4_56

Cited by 13 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Integration of web content mining into web usage mining is also possible [14,26]. In [26] the textual content of the web pages are extracted through frequent word sequence.…”

Section: Literature Workmentioning

confidence: 99%

Web Content Mining Techniques: A Survey

Johnson¹,

Gupta²

2012

IJCA

View full text Add to dashboard Cite

The Quest for knowledge has led to new discoveries and inventions. With the emergence of World Wide Web, it became a hub for all these discoveries and inventions. Web browsers became a tool to make the information available at our finger tips. As years passed World Wide Web became overloaded with information and it became hard to retrieve data according to the need. Web mining came as a rescue for the above problem. Web content mining is a subdivision under web mining. This paper deals with a study of different techniques and pattern of content mining and the areas which has been influenced by content mining. The web contains structured, unstructured, semi structured and multimedia data. This survey focuses on how to apply content mining on the above data. It also points out how web content mining can be utilized in web usage mining.

show abstract

“…Integration of web content mining into web usage mining is also possible [14,26]. In [26] the textual content of the web pages are extracted through frequent word sequence.…”

Section: Literature Workmentioning

confidence: 99%

Web Content Mining Techniques: A Survey

Johnson¹,

Gupta²

2012

IJCA

View full text Add to dashboard Cite

show abstract

“…), user knowledge, user skills and user interests. The usage data contains information that may be observed directly from the user's interaction with webbased system [6]. In the e-Learning environment the user data may also contain information about learner cognitive characteristic [2] and the usage data contains the learners' history of learning scenarios already passed together with information of corresponding test results.…”

Section: Learning Scenario Adaptationmentioning

confidence: 99%

XML-Based Learning Scenario Representation and Presentation in the Adaptive E-learning Environment

Kazienko¹,

Sobecki²

2004

Innovations in Applied Artificial Intelligence

View full text Add to dashboard Cite

E-Learning environments nowadays should adapt to their users and computer platforms they are using. In this paper the learning scenario collaborative recommendation based on consensus method using XML is presented. We present the XML description of the learning scenario and its components as well as the link database and XSL transformations for presenting them on given platforms in the way suitable for particular students.

show abstract

“…Agents not only interchange information, but they also possess their own knowledge. The presented method is closely related to the web recommendation ROSA project [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Multi-agent Web Recommendation Method Based on Indirect Association Rules

Kazienko

2004

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Recommendation systems often use association rules as main technique to discover useful links among the set of transactions, especially web usage data -historical user sessions. Presented in the paper new approach extends typical, direct association rules with indirect ones, which reflect associations existing "between" rather than "within" web user sessions. Both rule types are combined into complex rules which are used to obtain ranking lists needed for recommendation of pages in the web site. All recommendation tasks are distributed between many agents that communicate and transfer their knowledge one another.

show abstract

Link Recommendation Method Based on Web Content and Usage Mining

Cited by 13 publications

References 5 publications

Web Content Mining Techniques: A Survey

Web Content Mining Techniques: A Survey

XML-Based Learning Scenario Representation and Presentation in the Adaptive E-learning Environment

Multi-agent Web Recommendation Method Based on Indirect Association Rules

Contact Info

Product

Resources

About