Difference between revisions of "Kshitij: a Search and Page Recommendation System for Wikipedia"
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+ | {{Infobox work | ||
+ | | title = Kshitij: a Search and Page Recommendation System for Wikipedia | ||
+ | | date = 2008 | ||
+ | | authors = [[Phanikumar Bhamidipati]]<br />[[Kamalakar Karlapalem]] | ||
+ | | link = https://www.cse.iitb.ac.in/~comad/2008/PDFs/48-kshitij.pdf | ||
+ | }} | ||
'''Kshitij: a Search and Page Recommendation System for Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Phanikumar Bhamidipati]] and [[Kamalakar Karlapalem]]. | '''Kshitij: a Search and Page Recommendation System for Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Phanikumar Bhamidipati]] and [[Kamalakar Karlapalem]]. | ||
== Overview == | == Overview == | ||
Semantic information helps in identifying the context of a document. It will be interesting to find out how effectively this information can be used in recommending related documents in a partially annotated knowledge base such as [[Wikipedia]]. In this paper, authors present a generic recommendation system that utilizes the stored as well as dynamically extracted semantics from Wikipedia. The system generates two kinds of recommendations - for search results and for each page viewed by the user. It explores different meta-information such as links and [[categories]] in this process. Authors experiments show that the system is able to yield good quality recommendations and help in improving the user experience. Though the algorithms are tested on Wikipedia, external systems that do not have access to structured data can benefit from the recommendations. | Semantic information helps in identifying the context of a document. It will be interesting to find out how effectively this information can be used in recommending related documents in a partially annotated knowledge base such as [[Wikipedia]]. In this paper, authors present a generic recommendation system that utilizes the stored as well as dynamically extracted semantics from Wikipedia. The system generates two kinds of recommendations - for search results and for each page viewed by the user. It explores different meta-information such as links and [[categories]] in this process. Authors experiments show that the system is able to yield good quality recommendations and help in improving the user experience. Though the algorithms are tested on Wikipedia, external systems that do not have access to structured data can benefit from the recommendations. |
Revision as of 08:55, 27 February 2021
Authors | Phanikumar Bhamidipati Kamalakar Karlapalem |
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Publication date | 2008 |
Links | Original |
Kshitij: a Search and Page Recommendation System for Wikipedia - scientific work related to Wikipedia quality published in 2008, written by Phanikumar Bhamidipati and Kamalakar Karlapalem.
Overview
Semantic information helps in identifying the context of a document. It will be interesting to find out how effectively this information can be used in recommending related documents in a partially annotated knowledge base such as Wikipedia. In this paper, authors present a generic recommendation system that utilizes the stored as well as dynamically extracted semantics from Wikipedia. The system generates two kinds of recommendations - for search results and for each page viewed by the user. It explores different meta-information such as links and categories in this process. Authors experiments show that the system is able to yield good quality recommendations and help in improving the user experience. Though the algorithms are tested on Wikipedia, external systems that do not have access to structured data can benefit from the recommendations.