mixed initiative search - prof. dr. maarten de rijke
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Mixed initiative searchMaarten de Rijke
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www.amsterdamdatascience.nlMixed initiative search
• Based on joint work with David Graus, Evangelos Kanoulas, Edgar Meij, Daan Odijk, Ridho Reinanda, Manos Tsagkias, Christophe Van Gysel, Nikos Voskarides, Wouter Weerkamp, Marcel Worring, Masrour Zoghi
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It’s all about entities
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• Entities (people, locations, organizations, …) play central organizing role
• In search
• in web search, up to 70% of the queries are entity queries (Lin et al., 2012; Guo et al., 2011)
• in academic search, the proportion of queries that contain entities is over 93% (Li et al., 2016)
• increasingly, entities are retrievable items
It’s all about entities
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www.amsterdamdatascience.nlMixed initiative search
search
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www.amsterdamdatascience.nlMixed initiative search
search
mothers
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
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Where are we now?
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www.amsterdamdatascience.nlMixed initiative search
• Diverse intents
• You know an entity by the stuff it hangs out with
• Words
• Facets
• Entities
• Organizations relations
• …
Information needs around entities
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www.amsterdamdatascience.nlMixed initiative search
• Mine search logs to discover aspects
• Prioritizing entity display
• Providing direct action
• “Query-less” entity-oriented diversification
• Supporting complex search tasks
• Knowledge base design/construction
Discovering entity aspects
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www.amsterdamdatascience.nlMixed initiative search
• Automatically generate human-readable explanations of related entities
• Mine large volumes of text: fragments in which entities co-occur
Entity relations
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• Update description of long tail entity with information from range of sources
• Learn to adjust representations based on clicks
• Enriching the representation with additional descriptions helps improve retrieval
• Continuously updating the ranker helps
Entity updates
KBER
DCER
sim
sim
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www.amsterdamdatascience.nlMixed initiative search
• Unsupervised model construction, efficient entity capabilities query, and semantic matching between query terms and candidate entities
• Learn mappings between words and entities, as well as distributed representations of words and entities
• Words that are strongly evidential for particular products are projected nearby those products
• Very effective, highly scalable
Unsupervised entity ranking
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What is next?
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www.amsterdamdatascience.nlMixed initiative search
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www.amsterdamdatascience.nlMixed initiative search
document listaction at
querystate st
userenvironment
examine document list
generate implicit feedback
reward rt
implicit feedback
evaluation measureretrieval system
agentagent
action at
environment
reward rt
state st
Image taken from K. Hofmann, S. Whiteson, and M. de Rijke. Balancing exploration and exploitation in online learning to rank. In ECIR 2011, April 2011.
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www.amsterdamdatascience.nlMixed initiative search
• Go beyond the traditional search engine result page
• What should the search engine say?
• When should it switch?
With all of that information around an entity
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www.amsterdamdatascience.nlMixed initiative search
• Old-fashioned search engine result page (SERP)?
• Direct answer?
• Engage in a conversation?
• Generate a news article?
• Produce a timeline?
• Multi-document summary?
• Wikipedia page?
What should a search engine say?
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www.amsterdamdatascience.nlMixed initiative search
• Working with media professionals in an audiovisual archive
• It is their business to create narratives
• Wrapping extensive interviewing rounds with media professionals
• Next
• Annotate and mine semantic aspects of narratives media professionals create
• Learning templates for SERP generation
• Learning to re-order elements on the SERP
• Generating natural language to connect the elements
Example: narrative search
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www.amsterdamdatascience.nlMixed initiative search
Example: Engage in a conversation
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www.amsterdamdatascience.nlMixed initiative search
• Run an A/B test
• Explore online
• Learn from historical data (“counterfactual reasoning”)
When should should it switch?
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Would people buy it?
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www.amsterdamdatascience.nlMixed initiative search
document listaction at
querystate st
userenvironment
examine document list
generate implicit feedback
reward rt
implicit feedback
evaluation measureretrieval system
agentagent
action at
environment
reward rt
state st
Based on K. Hofmann, S. Whiteson, and M. de Rijke. Balancing exploration and exploitation in online learning to rank. In ECIR 2011, April 2011.
answer format+
answers
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www.amsterdamdatascience.nlMixed initiative search
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