Improving recency ranking using twitter data

  • Authors:
  • Yi Chang;Anlei Dong;Pranam Kolari;Ruiqiang Zhang;Yoshiyuki Inagaki;Fernanodo Diaz;Hongyuan Zha;Yan Liu

  • Affiliations:
  • Yahoo! Labs;Yahoo! Labs;Yahoo! Labs;Yahoo! Labs;Yahoo! Labs;Yahoo! Labs;Georgia Institute of Technology;University of Southern California

  • Venue:
  • ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
  • Year:
  • 2013

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Abstract

In Web search and vertical search, recency ranking refers to retrieving and ranking documents by both relevance and freshness. As impoverished in-links and click information is the the biggest challenge for recency ranking, we advocate the use of Twitter data to address the challenge in this article. We propose a method to utilize Twitter TinyURL to detect fresh and high-quality documents, and leverage Twitter data to generate novel and effective features for ranking. The empirical experiments demonstrate that the proposed approach effectively improves a commercial search engine for both Web search ranking and tweet vertical ranking.