Dynamic information retrieval modeling /

Dynamic information retrieval modeling / Grace Hui Yang, Marc Sloan, Jun Wang.

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Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these c...

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Bibliographic Details
Online Access: Full Text (via Morgan & Claypool)
Main Authors: Yang, Grace Hui (Author), Sloan, Marc (Author), Wang, Jun (Author)
Format: eBook
Language:English
Published: [San Rafael, California] : Morgan & Claypool, 2016.
Series:Synthesis lectures on information concepts, retrieval, and services (Online) ; # 49.
Subjects:
Information retrieval > Computer simulation.
Dynamic programming.
Table of Contents:
  • 1. Introduction
  • 1.1 Dynamics in information retrieval
  • 1.2 Challenges
  • 1.3 Overview of dynamic IR
  • 1.4 Aims of this book
  • 1.5 Structure
  • 2. Information retrieval frameworks
  • 2.1 Case study: multi-page search
  • 2.2 Static information retrievaL
  • 2.2.1 The ranking problem
  • 2.2.2 The diversification problem
  • 2.3 Interactive information retrieval
  • 2.3.1 The Rocchio algorithm
  • 2.3.2 Interactive probability ranking principle
  • 2.4 Dynamic information retrieval
  • 2.4.1 Reinforcement learning vs. dynamic IR modeling
  • 2.4.2 Markov decision process
  • 2.4.3 Partially observable Markov decision process
  • 2.4.4 Bandits models
  • 2.5 Modeling dynamic IR
  • 3. Dynamic IR for a single query
  • 3.1 Information filtering
  • 3.1.1 Relevance feedback
  • 3.1.2 Active learning
  • 3.1.3 Multi-page search
  • 3.2 Multi-armed bandits
  • 3.2.1 Exploration vs. exploitation
  • 3.2.2 Multi-armed bandit variations
  • 3.3 Related work
  • 4. Dynamic IR for sessions
  • 4.1 Session search
  • 4.1.1 Query change: a strong signal from the user
  • 4.1.2 Markov chains in sessions
  • 4.1.3 Two-way communication in sessions
  • 4.2 Modeling sessions in the dynamic IR framework
  • 4.2.1 States
  • 4.2.2 Actions
  • 4.2.3 Rewards
  • 4.3 Dual-agent stochastic game: putting users into retrieval models
  • 4.3.1 Framework formulation
  • 4.3.2 Observation functions
  • 4.3.3 Belief updates
  • 4.4 Retrieval for sessions
  • 4.4.1 Obtaining the policies by heuristics
  • 4.4.2 Obtaining the policies by joint optimization
  • 4.5 Related work
  • 5. Dynamic IR for recommender systems
  • 5.1 Collaborative filtering
  • 5.2 Static recommendation
  • 5.2.1 User-based approaches
  • 5.2.2 Probabilistic matrix factorization
  • 5.3 Dynamics in recommendation
  • 5.3.1 Objective function
  • 5.3.2 User dynamics
  • 5.3.3 Item selection via confidence bound
  • 5.4 Related work
  • 6. Evaluating dynamic IR systems
  • 6.1 IR evaluation
  • 6.2 Text retrieval conference (TREC)
  • 6.2.1 TREC interactive track
  • 6.2.2 TREC session track
  • 6.2.3 TREC dynamic domain (DD) track
  • 6.3 The water filling model
  • 6.4 The cube test
  • 6.4.1 Filling up the cube
  • 6.4.2 Stopping criteria
  • 6.5 Plotting the dynamic progress
  • 6.6 Related work
  • 7. Conclusion
  • Bibliography
  • Authors' biographies.

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