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Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
Making a simple question into a complicated query
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Making a simple question into a complicated query

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  • 1. Making a simple question into a complicated query Richard Boulton Lemur Consulting Ltd
  • 2. Making a simple question into a complicated query Richard Boulton Lemur Consulting Ltd
  • 3. Making a simple question into a complicated query Richard Boulton Lemur Consulting Ltd
  • 4. Only 20 minutes until Lunch
  • 5. Where shall we have Lunch?
  • 6. “Lunch”
  • 7. Assertion Complicated questions are easier to answer well.
  • 8. Assertion Complicated questions are easier to answer well accurately.
  • 9. Restaurant Pizza restaurant near Covent Garden, fairly cheap.
  • 10. Time for a real example http://mydeco.com/ Interior decoration site
  • 11. 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?
  • 12. Getting more from users
  • 13. Getting more from users Suggested search completions
  • 14. Getting more from users Facets
  • 15. 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?
  • 16. Getting more from users Personal data − Using details about the user directly. e.g., Postcode − Grouping users by similarity of interests
  • 17. Getting more from users Similarity search − “More like this” − Colour / image-based similarity
  • 18. 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”
  • 19. 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”.
  • 20. 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
  • 21. Answering complicated questions
  • 22. Answering complicated questions Getting the best answer − Good models − Careful design − Lots of tuning
  • 23. Answering complicated questions Getting an answer quickly − Good algorithms − Well matched data-structures − Plenty of machines − Plenty of RAM
  • 24. Questions, and then Lunch! Richard Boulton Lemur Consulting Ltd richard@lemurconsulting.com

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