Details

Autor(en) / Beteiligte

• Yang, Grace Hui
• Dong, Xuchu
• Luo, Jiyun
• Zhang, Sicong

Titel

Session search modeling by partially observable Markov decision process

Ist Teil von

• Information retrieval (Boston), 2018-02, Vol.21 (1), p.56-80

Ort / Verlag

Dordrecht: Springer Netherlands

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.