Ranking Documents with Query-Topic Sensitivity

被引：0

作者：

Hatakenaka, Shota ^{[1
]}

Shimada, Satoshi ^{[2
]}

Miura, Takao ^{[1
]}

机构：

[1] Hosei Univ, Dept Elect & Elect Engn, Tokyo, Japan

[2] Hosei Univ, Res Ctr Micronano Technol, Tokyo, Japan

来源：

2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY WORKSHOPS (WI-IAT WORKSHOPS 2012), VOL 3 | 2012年

关键词：

D O I：

10.1109/WI-IAT.2012.207

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, we discuss Query-Topic Sensitive Ranking algorithm, called Topic-Driven PageRank (TDPR), to inquire general documents based on a notion of importance. The main idea is that we extract knowledge from training data for multiple classification and build characteristic feature for each topic. By this approach, we get documents reflecting queries and topics within so that we can improve query results and to avoid topicdrift problems.

引用

页码：195 / 199

页数：5

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