Intelligent Search
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Intelligent Search

on

  • 2,098 views

ApacheCon 2009 talk describing methods for doing intelligent (well, really clever at least) search on items with no or poor meta-data. ...

ApacheCon 2009 talk describing methods for doing intelligent (well, really clever at least) search on items with no or poor meta-data.

The video of the talk should be available shortly on the ApacheCon web-site.

Statistics

Views

Total Views
2,098
Views on SlideShare
2,058
Embed Views
40

Actions

Likes
4
Downloads
34
Comments
0

3 Embeds 40

http://www.linkedin.com 27
https://www.linkedin.com 11
http://www.slideshare.net 2

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Intelligent Search Presentation Transcript

  • 1. Intelligent Search
  • 2. Intelligent Search (or at least really clever)
  • 3. Some Preliminaries • Text retrieval = matrix multiplication A: our corpus documents are rows terms are columns
  • 4. Some Preliminaries • Text retrieval = matrix multiplication A: our corpus documents are rows terms are columns for each document d: for each term t: sd += adt qt
  • 5. Some Preliminaries • Text retrieval = matrix multiplication A: our corpus documents are rows terms are columns sd = Σt adt qt
  • 6. Some Preliminaries • Text retrieval = matrix multiplication A: our corpus documents are rows terms are columns s=Aq
  • 7. More Preliminaries • Recommendation = Matrix multiply A: our users’ histories users are rows items are columns
  • 8. More Preliminaries • Recommendation = Matrix multiply A: our users’ histories users are rows items are columns Users who bought items in the list h also bought items in the list r
  • 9. More Preliminaries • Recommendation = Matrix multiply A: our users’ histories users are rows items are columns for each user u: for each item t1: for each item t2: rt1 += au,t1 au,t2 ht2
  • 10. More Preliminaries • Recommendation = Matrix multiply A: our users’ histories users are rows items are columns sd = Σt2 Σu au,t1 au,t2 qt2
  • 11. More Preliminaries • Recommendation = Matrix multiply A: our users’ histories users are rows items are columns s = A’ (A q)
  • 12. More Preliminaries • Recommendation = Matrix multiply A: our users’ histories users are rows items are columns s = (A’ A) q
  • 13. More Preliminaries • Recommendation = Matrix multiply A: our users’ histories users are rows items are columns s = (A’ A) q ish!
  • 14. Why so ish? • In real life, ish happens because: • Big data ... so we selectively sample • Sparse data ... so we smooth • Finite computers ... so we sparsify • Top-40 effect ... so we use some stats
  • 15. The same in spite of ish • The shape of the computation is unchanged • The cost of the computation is unchanged • Broad algebraic conclusions still hold
  • 16. Back to recommendations ...
  • 17. Dyadic Structure ● Functional – Interaction: actor -> item* ● Relational – Interaction ⊆ Actors x Items ● Matrix – Rows indexed by actor, columns by item – Value is count of interactions ● Predict missing observations
  • 18. Fundamental Algorithmics ● Cooccurrence ● A is actors x items, K is items x items ● Product has general shape of matrix ● K tells us “users who interacted with x also interacted with y”
  • 19. Fundamental Algorithmic Structure ● Cooccurrence ● Matrix approximation by factoring ● LLR
  • 20. But Wait ...
  • 21. But Wait ... Does it have to be that way?
  • 22. What we have: For a user who watched/bought/listened to this
  • 23. What we have: For a user who watched/bought/listened to this Sum over all other users who watched/bought/...
  • 24. What we have: For a user who watched/bought/listened to this Sum over all other users who watched/bought/... Add up what they watched/bought/listened to
  • 25. What we have: For a user who watched/bought/listened to this Sum over all other users who watched/bought/... Add up what they watched/bought/listened to And recommend that
  • 26. What we have: For a user who watched/bought/listened to this Sum over all other users who watched/bought/... Add up what they watched/bought/listened to And recommend that ish
  • 27. What we have: Add up what they watched/bought/listened to
  • 28. What we have: Add up what they watched/bought/listened to But wait, we can do that faster
  • 29. What we have: Add up what they watched/bought/listened to But wait, we can do that faster
  • 30. But why not ...
  • 31. But why not ...
  • 32. But why not ... Why just dyadic learning?
  • 33. But why not ... Why just dyadic learning? Why not triadic learning?
  • 34. But why not ... Why just dyadic learning? Why not p-adic learning?
  • 35. For example ● Users enter queries (A) – (actor = user, item=query) ● Users view videos (B) – (actor = user, item=video) ● AʼA gives query recommendation – “did you mean to ask for” ● BʼB gives video recommendation – “you might like these videos”
  • 36. The punch-line ● BʼA recommends videos in response to a query – (isnʼt that a search engine?) – (not quite, it doesnʼt look at content or meta-data)
  • 37. Real-life example ● Query: “Paco de Lucia” ● Conventional meta-data search results: – “hombres del paco” times 400 – not much else ● Recommendation based search: – Flamenco guitar and dancers – Spanish and classical guitar – Van Halen doing a classical/flamenco riff
  • 38. Real-life example
  • 39. Real-life example
  • 40. System Diagram Viewing Logs selective count t user video sampler Search Logs selective llr + Related videos count t user query-term sampler sparsify v => v1 v2... Related terms join on v => t1 t2... count user Hadoop
  • 41. Indexing Related terms v => t1 t2... Related videos v => v1 v2... join on Lucene Index video Video meta v => url title... Hadoop Lucene (+Katta?)
  • 42. Hypothetical Example ● Want a navigational ontology? ● Just put labels on a web page with traffic – This gives A = users x label clicks ● Remember viewing history – This gives B = users x items ● Cross recommend – BʼA = click to item mapping ● After several users click, results are whatever users think they should be
  • 43. Resources ● My blog – http://tdunning.blogspot.com/ ● The original LLR in NLP paper – Accurate Methods for the Statistics of Surprise and Coincidence (check on citeseer) ● Source code – Mahout project – contact me (tdunning@apache.org)