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| Content Provider | ACM Digital Library |
|---|---|
| Author | Liu, Yan Zha, Hongyuan Kolari, Pranam Zhang, Ruiqiang Dong, Anlei Inagaki, Yoshiyuki Chang, Yi Diaz, Fernanodo |
| Copyright Year | 2013 |
| 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. |
| Starting Page | 1 |
| Ending Page | 24 |
| Page Count | 24 |
| File Format | |
| ISSN | 21576904 |
| e-ISSN | 21576912 |
| DOI | 10.1145/2414425.2414429 |
| Volume Number | 4 |
| Issue Number | 1 |
| Journal | ACM Transactions on Intelligent Systems and Technology (TIST) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-02-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Recency ranking Twitter Tweet ranking |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Theoretical Computer Science |
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