Trulia SuggestsApril 4, 2013
Trulia
Big Data @ Trulia             31M users              Property data                  Images            Public record data  ...
Trulia
Trulia
Trulia
Trulia
Natural Fit for Personalization  One (long) search     – Sometimes very long (see: San Francisco market)     – Often multi...
Largest Companies
Limited Personalization
Prototype   Initially considered work the work surrounding the      Netflix Prize and KDD Cups…
PrototypeQuickly recognized…  - real-time higher priority than accuracy  - requiresbetter model of user click behavior
Prototyping   Unconstrained interface development…
Hide, Like, or Follow
Pick a house user community
Pick a house user community
Pick a house user community
Suggestions – Visual Interface
Suggestions – Assembling Lists
Trulia Suggests v2.0  More integration into the natural home buying process  More models, not just preference prediction  ...
Trulia             We’re hiring!         datascience@trulia.com
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A talk on Trulia Suggest by creator Todd Holloway hosted by SF Data Mining at Pandora.

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  • Everything logged
  • We were familiar with this work, which focuses on preference prediction, and without time constraints.
  • User’s are easily distracted
  • The interface decisions are at least as important as the algorithm decisions. Particularly early on. We didn’t want to be biased by our current FE (FE tech has developed rapidly) so we build a separate prototype using the latest tech and then found ways to carry elements back.
  • Hide because people often do repeat searches, like as the positive preference, and follow as a stronger like in which updates are sent
  • We originally planned to bootstrap the system using only behavior. But users are easily distracted. While we continue to build better models of user real estate search behavior, we decided just to ask users.
  • Recommendations themselves are displayed in a visual interface. Cards instead of lists. Inline photos. Bits of animation.
  • Lists are central to home buying. Already on IPad. Coming soon to rentals, mobile.
  • Suggests

    1. 1. Trulia SuggestsApril 4, 2013
    2. 2. Trulia
    3. 3. Big Data @ Trulia 31M users Property data Images Public record data Agent data Local data (crimes, transit, etc) Q&A data …
    4. 4. Trulia
    5. 5. Trulia
    6. 6. Trulia
    7. 7. Trulia
    8. 8. Natural Fit for Personalization One (long) search – Sometimes very long (see: San Francisco market) – Often multiple sessions – Lots to interact with on Trulia
    9. 9. Largest Companies
    10. 10. Limited Personalization
    11. 11. Prototype Initially considered work the work surrounding the Netflix Prize and KDD Cups…
    12. 12. PrototypeQuickly recognized… - real-time higher priority than accuracy - requiresbetter model of user click behavior
    13. 13. Prototyping Unconstrained interface development…
    14. 14. Hide, Like, or Follow
    15. 15. Pick a house user community
    16. 16. Pick a house user community
    17. 17. Pick a house user community
    18. 18. Suggestions – Visual Interface
    19. 19. Suggestions – Assembling Lists
    20. 20. Trulia Suggests v2.0 More integration into the natural home buying process More models, not just preference prediction More visual More entertaining
    21. 21. Trulia We’re hiring! datascience@trulia.com
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