Improving Recency Ranking Using Twitter Data

被引：14

作者：

Chang, Yi

Dong, Anlei

Kolari, Pranam

Zhang, Ruiqiang

Inagaki, Yoshiyuki

Diaz, Fernanodo

Zha, Hongyuan ^{[1
]}

Liu, Yan ^{[2
]}

机构：

[1] Georgia Inst Technol, Atlanta, GA 30332 USA

[2] Univ So Calif, Los Angeles, CA 90089 USA

来源：

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY | 2013年 / 4卷 / 01期

关键词：

Algorithms; Experimentation; Recency ranking; Twitter; tweet ranking;

D O I：

10.1145/2414425.2414429

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：24