Improving Recency Ranking Using Twitter Data

Y. Chang, A. Dong, P. Kolari, R. Zhang, Y. Inagaki, F. Diaz, H. Zha, Y. Liu
ACM Transactions Intelligent Systems Technology, February 2013
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} }