Session search modeling by partially observable Markov decision process

被引：8

作者：

Yang, Grace Hui ^{[1
]}

Dong, Xuchu ^{[1
,2
]}

Luo, Jiyun ^{[1
]}

Zhang, Sicong ^{[1
]}

机构：

[1] Georgetown Univ, Dept Comp Sci, Washington, DC 20057 USA

[2] Jilin Univ, Coll Comp Sci & Technol, Changchun, Jilin, Peoples R China

来源：

INFORMATION RETRIEVAL JOURNAL | 2018年 / 21卷 / 01期

关键词：

Session search; Dynamic IR modeling; POMDP;

D O I：

10.1007/s10791-017-9316-8

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Session search, the task of document retrieval for a series of queries in a session, has been receiving increasing attention from the information retrieval research community. Session search exhibits the properties of rich user-system interactions and temporal dependency. These properties lead to our proposal of using partially observable Markov decision process to model session search. On the basis of a design choice schema for states, actions and rewards, we evaluate different combinations of these choices over the TREC 2012 and 2013 session track datasets. According to the experimental results, practical design recommendations for using PODMP in session search are discussed.

引用

页码：56 / 80

页数：25

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