Difference between revisions of "Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History"
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+ | | title = Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History | ||
+ | | date = 2010 | ||
+ | | authors = [[Mitsumasa Kondo]]<br />[[Akimichi Tanaka]]<br />[[Tadasu Uchiyama]] | ||
+ | | doi = 10.1145/1810617.1810682 | ||
+ | | link = https://dl.acm.org/citation.cfm?doid=1810617.1810682 | ||
+ | }} | ||
'''Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Mitsumasa Kondo]], [[Akimichi Tanaka]] and [[Tadasu Uchiyama]]. | '''Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Mitsumasa Kondo]], [[Akimichi Tanaka]] and [[Tadasu Uchiyama]]. | ||
== Overview == | == Overview == | ||
This paper proposes a method that can extract user interests from the user's Web browsing history. Authors method allows easy access to multiple content domains such as blogs, movies, QA sites, etc. since the user does not need to input a separate search query in each domain/site. To extract user interests, the method first extracts candidate keyphrases from the user's web browsed documents. Second, important keyphrases obtained from a link structure analysis of [[Wikipedia]] content is extracted from the main contents of web documents. This technique is based on the idea that important keyphrases in Wikipedia are important keyphrases in the real world. Finally, keyphrases contained in the documents important to the user are set in order as user interests. An experiment shows that method offers improvements over a conventional method and can recommend interests attractive to the user. | This paper proposes a method that can extract user interests from the user's Web browsing history. Authors method allows easy access to multiple content domains such as blogs, movies, QA sites, etc. since the user does not need to input a separate search query in each domain/site. To extract user interests, the method first extracts candidate keyphrases from the user's web browsed documents. Second, important keyphrases obtained from a link structure analysis of [[Wikipedia]] content is extracted from the main contents of web documents. This technique is based on the idea that important keyphrases in Wikipedia are important keyphrases in the real world. Finally, keyphrases contained in the documents important to the user are set in order as user interests. An experiment shows that method offers improvements over a conventional method and can recommend interests attractive to the user. |
Revision as of 03:25, 11 June 2019
Authors | Mitsumasa Kondo Akimichi Tanaka Tadasu Uchiyama |
---|---|
Publication date | 2010 |
DOI | 10.1145/1810617.1810682 |
Links | Original |
Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History - scientific work related to Wikipedia quality published in 2010, written by Mitsumasa Kondo, Akimichi Tanaka and Tadasu Uchiyama.
Overview
This paper proposes a method that can extract user interests from the user's Web browsing history. Authors method allows easy access to multiple content domains such as blogs, movies, QA sites, etc. since the user does not need to input a separate search query in each domain/site. To extract user interests, the method first extracts candidate keyphrases from the user's web browsed documents. Second, important keyphrases obtained from a link structure analysis of Wikipedia content is extracted from the main contents of web documents. This technique is based on the idea that important keyphrases in Wikipedia are important keyphrases in the real world. Finally, keyphrases contained in the documents important to the user are set in order as user interests. An experiment shows that method offers improvements over a conventional method and can recommend interests attractive to the user.