Effective Personalized Web Mining by Utilizing The Most Utilized Data

TL;DR

This paper proposes a personalized web mining approach that ranks links based on user interaction data, such as visit frequency and time spent, to improve search relevance.

Contribution

It introduces a method that utilizes user click and time data to rank web links, enhancing personalization in web search results.

Findings

Links are ranked based on visit count and time spent.

The approach improves relevance of search results.

Personalization increases user satisfaction.

Abstract

Looking into the growth of information in the web it is a very tedious process of getting the exact information the user is looking for. Many search engines generate user profile related data listing. This paper involves one such process where the rating is given to the link that the user is clicking on. Rather than avoiding the uninterested links both interested links and the uninterested links are listed. But sorted according to the weightings given to each link by the number of visit made by the particular user and the amount of time spent on the particular link.