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.
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@article{tist:twitter,
year = {2013},
volume = {4},
url = {http://doi.acm.org/10.1145/2414425.2414429},
title = {Improving Recency Ranking Using Twitter Data},
publisher = {ACM},
pages = {4:1--4:24},
numpages = {24},
number = {1},
month = {February},
journal = {ACM Trans. Intell. Syst. Technol.},
issue_date = {January 2013},
issn = {2157-6904},
doi = {10.1145/2414425.2414429},
author = {Chang, Yi and Dong, Anlei and Kolari, Pranam and Zhang, Ruiqiang and Inagaki, Yoshiyuki and Diaz, Fernando and Zha, Hongyuan and Liu, Yan},
articleno = {4},
address = {New York, NY, USA},
acmid = {2414429}
}