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- 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)

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