Learning for Search Results Diversification in Twitter

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9659
• 期：1
• 页码：251-264
• 全文大小：505 KB
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• 作者单位：Ying Wang (18)
  Zhunchen Luo (18)
  Yang Yu (18)
  
  18. China Defense Science and Technology Information Center, Beijing, 100142, China
  
• 丛书名：Web-Age Information Management
• ISBN：978-3-319-39958-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9659

文摘

Diversifying the results retieved is an effective approach to tackling users’ information needs in Twitter, which typically described by query phrase are often ambiguous and have more than one interpretation. Due to tweets being often very short and lacking in reliable grammatical sytle, it reduces the effectiveness of traditional IR and NLP techniques. However, Twitter, as a social media, also presents interesting opportunies for this task (for example the author information such as the number of statuses). In this paper, we firstly address diversitication of the search results in Twitter with a learning method and explore a series of diversity features describing the relationship between tweets which include tweet content, sub-topic of tweet and the Twitter specific social information such as hashtags. The experimental results on the Tweets2013 datasets demonstrate the effectiveness of the learning approach. Additionally, the Twitter retrieval task achieves improvement by taking into account the diversity features. Finally, we find the sub-topic and Twitter specific social features can help solve the diversity task, especially the post time, hashtags of tweet and the location of author.