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Making a simple question into a complicated query Richard Boulton Lemur Consulting Ltd

Making a simple question into a complicated query

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Page 1: Making a simple question into a complicated query

Making a simple question into a complicated query

Richard BoultonLemur Consulting Ltd

Page 2: Making a simple question into a complicated query

Making a simple question into a complicated query

Richard BoultonLemur Consulting Ltd

Page 3: Making a simple question into a complicated query

Making a simple question into a complicated query

Richard BoultonLemur Consulting Ltd

Page 4: Making a simple question into a complicated query

Only 20 minutes until Lunch

Page 5: Making a simple question into a complicated query

Where shall we have Lunch?

Page 6: Making a simple question into a complicated query

“Lunch”

Page 7: Making a simple question into a complicated query

Assertion

Complicated questions areeasier to answer well.

Page 8: Making a simple question into a complicated query

Assertion

Complicated questions areeasier to answer well accurately.

Page 9: Making a simple question into a complicated query

� Restaurant � Pizza restaurant near Covent Garden, fairly cheap.

Page 10: Making a simple question into a complicated query

Time for a real example

http://mydeco.com/

Interior decoration site

Page 11: Making a simple question into a complicated query

� Users type: “Sofa”

� We'd prefer them to ask questions like: “Red velvet, three seater, sofa, from a supplier who can deliver to central Cambridge at a weekend”.

� How can we move to this kind of search?

Page 12: Making a simple question into a complicated query

Getting more from users

Page 13: Making a simple question into a complicated query

Getting more from users

� Suggested search completions

Page 14: Making a simple question into a complicated query

Getting more from users

� Facets

Page 15: Making a simple question into a complicated query

Getting more from users

� Facets

� Which facets to display?

− Depends on the user.

� Which facet values are interesting?

− A particularly fun problem for continuous numeric values, like price.

� How many values should we display?

− Based on likelihood of any being useful?

Page 16: Making a simple question into a complicated query

Getting more from users

� Personal data

− Using details about the user directly.

� e.g., Postcode

− Grouping users by similarity of interests

Page 17: Making a simple question into a complicated query

Getting more from users

� Similarity search

− “More like this”

− Colour / image-based similarity

Page 18: Making a simple question into a complicated query

Behind the scenes

� Applying our own bias.

− Perhaps we want to push some items

− Perhaps we want to avoid other items

− Perhaps some items go well together

− Behave like a shop assistant

− “Product Rank”

Page 19: Making a simple question into a complicated query

Behind the scenes

� Categorisation

− User asks for “Sofa”.

− We search for “Products categorised as one of the sofa subcategories, based on the output of a machine learning system trained with some human judgements”.

Page 20: Making a simple question into a complicated query

Behind the scenes

� Variety

− Don't display lots of very similar items

− Give the user a choice

− But don't display irrelevant junk, either!

− Need some way to measure variety

Page 21: Making a simple question into a complicated query

Answering complicated questions

Page 22: Making a simple question into a complicated query

Answering complicated questions

� Getting the best answer

− Good models

− Careful design

− Lots of tuning

Page 23: Making a simple question into a complicated query

Answering complicated questions

� Getting an answer quickly

− Good algorithms

− Well matched data-structures

− Plenty of machines

− Plenty of RAM

Page 24: Making a simple question into a complicated query

Questions, and then Lunch!

Richard BoultonLemur Consulting Ltd

[email protected]