Konyushkova

IntroductionSystem overviewSciNet backend

Results

Towards Exploratory Searchof Scientific Information

Ksenia Konyushkova

Ksenia Konyushkova Towards Exploratory Search of Scientific Information


Results

Table of contents

1 Introduction

2 System overview

3 SciNet backendRetrievalKeyword ExplorationDocument Exploration

4 Results



Results

Helsinki Institute for Information Technology andUniversity of Helsinki, Department of Computer Science

Directing Exploratory Search: Reinforcement Learning from UserInteractions with Keywords

Dorota G lowackaTuukka Ruotsalo

Ksenia KonyushkovaKumaripaba Athukorala

Samuel KaskiGiulio Jacucci



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Introduction

Goal of the system:

Support exploratory information seeking behavior of researchers byoffering tools to assist in navigating through complex informationspaces

Techniques:

Reinforcement Learning

Optimized Visualization



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Introduction

Goal of the system:


Techniques:





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Introduction

Goal of the system:


Techniques:





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Introduction

Goal of the system:


Techniques:





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SciNet: System Interface



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Dataflow

Figure: Overview of data flow in the exploratory search system



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RetrievalKeyword ExplorationDocument Exploration

Retrieving and Ranking Documents

Probabilistic multinomial unigram language model

MLE:

P(k |Mdj ) =i∏

ki∈kwi Pmle(ki |Mdj ),

Bayesian Dirichlet smoothing:

Pµ(k|dj) =c(k ; dj) + µp(k |C )∑

k c(k ; dj) + µ,



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Probabilistic multinomial unigram language modelMLE:

P(k |Mdj ) =i∏




k c(k ; dj) + µ,



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Probabilistic multinomial unigram language modelMLE:

P(k |Mdj ) =i∏




k c(k ; dj) + µ,



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Machine Learning:

Supervised LearningUnsupervised LearningReinforcement Learning

Reinforcement Learning:

agents take actions in the environment to maximize the rewardExploration-Exploitation paradigmMilti-armed bandit problem: greedy, epsilon-greedy, UCB-1,UCB-tuned and so on

Figure: Multi-armed bandits (Microsoft research)



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Machine Learning:

Supervised Learning

Unsupervised LearningReinforcement Learning






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Machine Learning:

Supervised LearningUnsupervised Learning







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Machine Learning:







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Machine Learning:







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Machine Learning:



agents take actions in the environment to maximize the reward

Exploration-Exploitation paradigmMilti-armed bandit problem: greedy, epsilon-greedy, UCB-1,UCB-tuned and so on




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Machine Learning:



agents take actions in the environment to maximize the rewardExploration-Exploitation paradigm

Milti-armed bandit problem: greedy, epsilon-greedy, UCB-1,UCB-tuned and so on




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Machine Learning:




Figure: Multi-armed bandits (Microsoft research)Ksenia Konyushkova Towards Exploratory Search of Scientific Information


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Keyword Exploration (query drift)

Learning to rank: initial document retrieval returns 300documents

Receive feedback from the user

Keywords representation - tfidf

Exploration - LinRel (Auer, 2002)



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LinRel

LinRel algorithm (Auer, 2002):

estimate weight vector w by solving a linear regression

r = X · w

calculate estimated relevance score ri = xi · wcalculate upper confidence bound:

ri + γσi

choose keywords with highest upper confidence bound



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LinRel



r = X · w


ri + γσi




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LinRel



r = X · w

calculate estimated relevance score ri = xi · w

calculate upper confidence bound:

ri + γσi




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LinRel



r = X · w


ri + γσi




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LinRel



r = X · w


ri + γσi




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GP UCB

Gaussian Process Bandits

Present to the user the object that maximizes

argmax{µi +√β · σi},

whereµ = K∗K

−1r ,

σ = K∗∗ − K∗K−1KT

∗ .



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GP UCB




whereµ = K∗K

−1r ,

σ = K∗∗ − K∗K−1KT

∗ .



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GP UCB




whereµ = K∗K

−1r ,

σ = K∗∗ − K∗K−1KT

∗ .



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GP SOM

Hierarchical Gaussian Process Bandits with Self-OrganizingMaps

Figure: ImSe interfaceKsenia Konyushkova Towards Exploratory Search of Scientific Information


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Document Exploration (diversity)

Assumption: relevance of a keyword - relevance of all thedocuments containing this keyword

α - success measure, β - failure measure

Thompson sampling for Bernoulli bandit with Betadistribution (Thompson, 1933; Chapelle, Li, 2011):each document is bandit arm with a Beta distribution -Beta(α, β),



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Intent Modeling

Figure: Illustration of intent modeling



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User studies

”You are writing an essay describing the field of ”robotics”. Thisessay should include at least three subfields of ”robotics”, threeapplication areas of ”robotics” and three algorithms commonlyused in the field of ”robotics”.”



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Precision results

Figure: Illustration of precision measure of Baseline and SciNet in termsof relevance, novelty and obviousness



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Recall results

Figure: Illustration of recall measure of Baseline and SciNet in terms ofrelevance, novelty and obviousness



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F-measure results

Figure: Illustration of F-measure measure of Baseline and SciNet in termsof relevance, novelty and obviousness



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Keywords results

Figure: Cumulative amount of shown and manipulated keywords inSciNet system



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Conclusions

Interactive information retrieval system


Radar Layout

Performance in Precision, Recall and F-measure in terms ofRelevance, Novelty and Obviousness



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Acknowledgments

The data used in the experiments is derived from the Web of Science prepared by THOMSON REUTERS, Inc.,Philadelphia, Pennsylvania, USA: Copyright THOMSON REUTERS, 2011. All rights reserved; the Digital Libraryof the Association of Computing Machinery (ACM); the Digital Library of Institute of Electrical and ElectronicsEngineers (IEEE), and the Digital Library of Springer.

The work has been partly supported by the Academy of Finland under the Finnish Center of Excellence in

Computational Inference Research (COIN), by the Finnish Funding Agency for Technology and Innovation under

project D2I, and by the IST Programme of the European Community under the PASCAL Network of Excellence.



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Thanks for your attention!
