%0 Journal Article %1 citeulike:14057418 %A Chang, Yi %A Dong, Anlei %A Kolari, Pranam %A Zhang, Ruiqiang %A Inagaki, Yoshiyuki %A Diaz, Fernanodo %A Zha, Hongyuan %A Liu, Yan %C New York, NY, USA %D 2013 %I ACM %J ACM Trans. Intell. Syst. Technol. %K ranking social-search twitter %N 1 %R 10.1145/2414425.2414429 %T Improving Recency Ranking Using Twitter Data %U http://dx.doi.org/10.1145/2414425.2414429 %V 4 %X 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.

@article{citeulike:14057418, 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.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Chang, Yi and Dong, Anlei and Kolari, Pranam and Zhang, Ruiqiang and Inagaki, Yoshiyuki and Diaz, Fernanodo and Zha, Hongyuan and Liu, Yan}, biburl = {https://www.bibsonomy.org/bibtex/226f47228281b49822a2cbc56b2658e05/aho}, citeulike-article-id = {14057418}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2414429}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2414425.2414429}, doi = {10.1145/2414425.2414429}, interhash = {23cd2614788c55e16d05b59900a99dd6}, intrahash = {26f47228281b49822a2cbc56b2658e05}, issn = {2157-6904}, journal = {ACM Trans. Intell. Syst. Technol.}, keywords = {ranking social-search twitter}, month = feb, number = 1, posted-at = {2016-06-06 03:24:32}, priority = {2}, publisher = {ACM}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Improving Recency Ranking Using Twitter Data}}, url = {http://dx.doi.org/10.1145/2414425.2414429}, volume = 4, year = 2013 }