Maarten De Rijke (http://staff.science.uva.nl/~mdr/) describes how information retrieval is a data science discipline. Outlines the connection between his group and other data computer science disciplines and paints a vision for the future of information retrieval.

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Data Science
Research Center
Information Retrieval
Maarten de Rijke

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Information retrieval
• Information retrieval: computational methods
for analyzing, understanding and enabling
effective human interaction with information
sources
• Search engines: the most visible instantiation
of information retrieval theories, models and
algorithms

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Information retrieval
• Analysis
– Content, Structure, Semantics, Behavior
• Synthesis
– Aggregation, fusion, learning to rank
• Evaluation
– Offline evaluation, user-centered studies, online
evaluation

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Local highlights
• Analysis
– Ranking social content (sentiment, ―framing‖, reputation,
personality traits)
– Semantic search (entities, events)
– Online classification
– Log analysis with external partners (blog, multimedia,
location, …)
• Synthesis
– Online learning to rank
– Data fusion
– Aggregated search / vertical search
• Evaluation
– Interleaving
– Automatic generation of test sets

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Where is information
retrieval located?
Visual
Analytics
Business
Analytics
Decision
Theory
Understand
and decide
Distributed
Processing
Data
Reasoning
Knowledge
representati
on
Large Scale
Databases
Store and
process
Software
Eng.
System /
Network
Eng.
Analyze
and model
Multimedia
Retrieval
Modeling
and
simulation
Information
Retrieval
Machine
Learning

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What is next?
The most interesting
way to view the
world, is live

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What’s next?
• Real-time search
• Ranking and aggregation with subjective
criteria — sentiment, framing, personality
• Self-learning search engines — through
naturally occurring interactions with users
• ―I am the query‖
• See our demo