Discovering unexpected information on the basis of popularity/unpopularity analysis of coordinate objects and their relationships
|Discovering unexpected information on the basis of popularity/unpopularity analysis of coordinate objects and their relationships|
|Author(s)||Tsukuda K., Ohshima H., Yamamoto M., Iwasaki H., Tanaka K.|
|Published in||Proceedings of the ACM Symposium on Applied Computing|
|Keyword(s)||Coordinate term, Unexpected information, Wikipedia (Extra: Coordinate term, Jackson, Keyword queries, News articles, Unexpected information, User input, User's interest, Wikipedia, Websites, Query processing)|
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Discovering unexpected information on the basis of popularity/unpopularity analysis of coordinate objects and their relationships is a 2013 conference paper written in English by Tsukuda K., Ohshima H., Yamamoto M., Iwasaki H., Tanaka K. and published in Proceedings of the ACM Symposium on Applied Computing.
Although many studies have addressed the problem of finding Web pages seeking relevant and popular information from a query, very few have focused on the discovery of unexpected information. This paper provides and evaluates methods for discovering unexpected information for a keyword query. For example, if the user inputs "Michael Jackson," our system first discovers the unexpected related term "karate" and then returns the unexpected information "Michael Jackson is good at karate." Discovering unexpected information is useful in many situations. For example, when a user is browsing a news article on the Web, unexpected information about a person associated with the article can pique the user's interest. If a user is sightseeing or driving, providing unexpected, additional information about a building or the region is also useful. Our approach collects terms related to a keyword query and evaluates the degree of unexpectedness of each related term for the query on the basis of (i) the relationships of coordinate terms of both the keyword query and related terms, and (ii) the degree of popularity of each related term. Experimental results show that considering these two factors are effective for discovering unexpected information. Copyright 2013 ACM.
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