{"title":"有趣的事实:自动琐事事实提取从维基百科","authors":"David Tsurel, D. Pelleg, Ido Guy, Dafna Shahaf","doi":"10.1145/3018661.3018709","DOIUrl":null,"url":null,"abstract":"A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia. In this paper, we formalize a notion of trivia-worthiness and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage of Wikipedia's category structure, and rank an entity's categories by their trivia-quality. Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of \"good trivia\" 45% higher than prior work. Search-page tests show a 22% decrease in bounce rates and a 12% increase in dwell time, proving our facts hold users' attention.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Fun Facts: Automatic Trivia Fact Extraction from Wikipedia\",\"authors\":\"David Tsurel, D. Pelleg, Ido Guy, Dafna Shahaf\",\"doi\":\"10.1145/3018661.3018709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia. In this paper, we formalize a notion of trivia-worthiness and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage of Wikipedia's category structure, and rank an entity's categories by their trivia-quality. Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of \\\"good trivia\\\" 45% higher than prior work. Search-page tests show a 22% decrease in bounce rates and a 12% increase in dwell time, proving our facts hold users' attention.\",\"PeriodicalId\":344017,\"journal\":{\"name\":\"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3018661.3018709\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3018661.3018709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fun Facts: Automatic Trivia Fact Extraction from Wikipedia
A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia. In this paper, we formalize a notion of trivia-worthiness and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage of Wikipedia's category structure, and rank an entity's categories by their trivia-quality. Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of "good trivia" 45% higher than prior work. Search-page tests show a 22% decrease in bounce rates and a 12% increase in dwell time, proving our facts hold users' attention.