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?
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
