Designing States, Actions, and Rewards for Using POMDP in Session Search

Luo, Jiyun; Zhang, Sicong; Dong, Xuchu; Yang, Hui

doi:10.1007/978-3-319-16354-3_58

Cited by 16 publications

(23 citation statements)

References 18 publications

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“…The dynamicity in the search session is multidimensional, including users whose behavior might evolve, documents in which content might change, and their context-related relevance with respect to the information need. To tackle this challenge, several approaches aiming at leveraging sequential actions are proposed using Markov Decision models (POMDP) [45,75,139], pattern detection [97], or learning-to-rank methods [50,134].…”

Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

On the Collaboration Support in Information Retrieval

Soulier

Tamine

2017

ACM Comput. Surv.

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Collaborative Information Retrieval (CIR) is a well-known setting in which explicit collaboration occurs among a group of users working together to solve a shared information need. This type of collaboration has been deemed as bene cial for complex or exploratory search tasks. With the multiplicity of factors impacting on the search e ectiveness (e.g., collaborators' interactions or the individual perception of the shared information need), CIR gives rise to several challenges in terms of collaboration support through algorithmic approaches. More particularly, CIR should allow us to satisfy the shared information need by optimizing the collaboration within the search session over all collaborators, while ensuring that both mutually bene cial goals are reached and that the cognitive cost of the collaboration does not impact the search e ectiveness. In this survey, we propose an overview of CIR with a particular focus on the collaboration support through algorithmic approaches. The objective of this article is (a) to organize previous empirical studies analyzing collaborative search with the goal to provide useful design implications for CIR models, (b) to give a picture of the CIR area by distinguishing two main categories of models using the collaboration mediation axis, and (c) to point out potential perspectives in the domain.

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Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

On the Collaboration Support in Information Retrieval

Soulier

Tamine

2017

ACM Comput. Surv.

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“…MDPs have been used to model diverse IR problems, e.g., to explicitly model user behavior in session search [16,28,29]. Jin et al [21] utilize reinforcement learning and MDPs to improve ranking over multiple search result pages.…”

Section: Related Workmentioning

confidence: 99%

“…Guan et al [16] decrease weights of new terms based on past rewards, whereas Luo et al [28] model session search as a dual-agent stochastic game. Investigating the design choices for MDPs, they find that technology selection and explicit feedback are most effective in session search [29].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Query Modeling for Related Content Finding

Odijk

Meij

Sijaranamual

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Very recently, efforts have been made in modeling session search using the Partially Observable Markov Decision Process (POMDP) [172,173,248]. This line of research investigated the best ways to design the states, actions, and rewards within a POMDP framework for complex information retrieval tasks.…”

Section: Markov Models For Predictive Web Analyticsmentioning

confidence: 99%

Using Contextual Information to Understand Searching and Browsing Behavior

Kiseleva

2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Designing States, Actions, and Rewards for Using POMDP in Session Search

Cited by 16 publications

References 18 publications

On the Collaboration Support in Information Retrieval

On the Collaboration Support in Information Retrieval

Dynamic Query Modeling for Related Content Finding

Using Contextual Information to Understand Searching and Browsing Behavior

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