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I
Job recommendations are significantly different
Rapid inventory growth - Millions of new jobs discovered every day
Job recommendations are significantly different
Rapid inventory growth - Millions of new jobs discovered every day
~ 1.5 m...
Job recommendations are significantly different
Rapid inventory growth - Millions of new jobs discovered every day
~ 1.5 m...
Job recommendations are significantly different
Rapid inventory growth - Millions of new jobs discovered every day
~ 1.5 m...
Compute similarity
For ui
In {Users}
For uj
In {Users}
SIMi,j
= compute_similarity(ui,
uj
)
→
→
→
∩ ∪
Items[Ui
] = {x1
, x2
, ..xn
}
H
minhashH
(Ui
)= min{ x∈Itemsi
| H(x) }
Similarity(U1, U2) = 1, if minhash(U1) == minhash(U2)
Similarity(U1, U2) = 0, otherwise
This is an unbiased estimator
Similarity(U1, U2) = 1, if minhash(U1) == minhash(U2)
Similarity(U1, U2) = 0,
Hk
Hk
Prob(minhashH
(Ui
) == minhashH
(Uj
)) = J(Ui
, Uj
)
user → {job1, job2, job3,..}
H = {H1
, H2
, ..H20
}
for user in Users
for hash in H
minhash[hash] = min{x∈Itemsi
| hash(x)}
For ui
In {Users}
For uj
In {Users}
SIMi,j
= compute_similarity(ui,
uj
)
user1 → (111, 123, 134, 148, ..129)
user2 → (101, 123, 139, 148, ..135)
user3 → (191, 103, 126, 108, ..119)
user4 → (191, ...
user → {cluster}
cluster → {users}
123 → (user1, user2)
148 → (user1, user2)
129 → (user1, user4)
191 → (user3, user4)
...
→
→
user1 → {job1, job2}
user2 → {job2, job3, job5}
123 → {user1, user2}
→
user1 → {job1, job2}
user2 → {job2, job3, job5}
123 → {job1, job2, job3, job5}
1. user → {cluster}
user → {cluster} user1 → {111, 123, ..}
111 → {job5, job2, job9}
123 → {job1, job2, job3, job5}
{job2, job5, job9, job1, job3}
→
→
{job2, job5, job9, job1, job3}
1.
→
→ {101, 121}
→
→ {101, 121}
{“Software Engineer”,
“Java Developer”, “Python Developer”}
→
→ {101, 121}
{“Software Engineer”,
“Java Developer”, “Python Developer”}
minhash({“Software Engineer”, “Java Developer”,...
→
→ {101, 121}
{“Software Engineer”,
“Java Developer”, “Python Developer”}
minhash({“Software Engineer”, “Java Developer”,...
→
minhash({“Software Engineer”, “Java Developer”,
“Python Developer”}) → {99,121}
99 → add {“Software Engineer”, “Java Dev...
{“Software Engineer”, “Java Developer”,
“Python Developer”} {99, 121}
99 → {“Software Engineer”, “Java Developer”, “Python...
→ {99, 131}
{“Software Engineer”, “Java Developer”,
“Python Developer”}
→
→
→
→
→
→
●
●
1. http://go.indeed.com/docservice
→
→
→
→
→
●
●
●
Engineering blog & talks http://indeed.tech
Open Source http://opensource.indeedeng.io
Careers http://indeed.jobs
Twitter ...
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
Data Day Texas - Recommendations
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Data Day Texas - Recommendations

Preetha Appan is the technical lead of the recommendations team at Indeed. Her past contributions to Indeed's job and resume search engines include keyword tokenization improvements, query expansion features, and major infrastructure and performance improvements. She enjoys working on challenging problems in machine learning and information retrieval.

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Data Day Texas - Recommendations

  1. 1. I
  2. 2. Job recommendations are significantly different Rapid inventory growth - Millions of new jobs discovered every day
  3. 3. Job recommendations are significantly different Rapid inventory growth - Millions of new jobs discovered every day ~ 1.5 million new users visit indeed every day
  4. 4. Job recommendations are significantly different Rapid inventory growth - Millions of new jobs discovered every day ~ 1.5 million new users visit indeed every day Average lifespan of a job is ~30 days
  5. 5. Job recommendations are significantly different Rapid inventory growth - Millions of new jobs discovered every day ~ 1.5 million new users visit indeed every day Average lifespan of a job is ~30 days One job posting usually meant to hire one individual
  6. 6. Compute similarity For ui In {Users} For uj In {Users} SIMi,j = compute_similarity(ui, uj )
  7. 7. → →
  8. 8.
  9. 9. ∩ ∪
  10. 10. Items[Ui ] = {x1 , x2 , ..xn } H minhashH (Ui )= min{ x∈Itemsi | H(x) }
  11. 11. Similarity(U1, U2) = 1, if minhash(U1) == minhash(U2) Similarity(U1, U2) = 0, otherwise This is an unbiased estimator
  12. 12. Similarity(U1, U2) = 1, if minhash(U1) == minhash(U2) Similarity(U1, U2) = 0,
  13. 13. Hk Hk Prob(minhashH (Ui ) == minhashH (Uj )) = J(Ui , Uj )
  14. 14. user → {job1, job2, job3,..}
  15. 15. H = {H1 , H2 , ..H20 } for user in Users for hash in H minhash[hash] = min{x∈Itemsi | hash(x)}
  16. 16. For ui In {Users} For uj In {Users} SIMi,j = compute_similarity(ui, uj )
  17. 17. user1 → (111, 123, 134, 148, ..129) user2 → (101, 123, 139, 148, ..135) user3 → (191, 103, 126, 108, ..119) user4 → (191, 103, 126, 108, ..129) ...
  18. 18. user → {cluster} cluster → {users}
  19. 19. 123 → (user1, user2) 148 → (user1, user2) 129 → (user1, user4) 191 → (user3, user4) ...
  20. 20.
  21. 21. → user1 → {job1, job2} user2 → {job2, job3, job5} 123 → {user1, user2}
  22. 22. → user1 → {job1, job2} user2 → {job2, job3, job5} 123 → {job1, job2, job3, job5}
  23. 23. 1. user → {cluster}
  24. 24. user → {cluster} user1 → {111, 123, ..}
  25. 25. 111 → {job5, job2, job9} 123 → {job1, job2, job3, job5} {job2, job5, job9, job1, job3} → →
  26. 26. {job2, job5, job9, job1, job3}
  27. 27. 1.
  28. 28. → → {101, 121}
  29. 29. → → {101, 121} {“Software Engineer”, “Java Developer”, “Python Developer”}
  30. 30. → → {101, 121} {“Software Engineer”, “Java Developer”, “Python Developer”} minhash({“Software Engineer”, “Java Developer”, “Python Developer”}) → {99, 135}
  31. 31. → → {101, 121} {“Software Engineer”, “Java Developer”, “Python Developer”} minhash({“Software Engineer”, “Java Developer”, “Python Developer”}) → {99, 135} → {99, 121}
  32. 32. → minhash({“Software Engineer”, “Java Developer”, “Python Developer”}) → {99,121} 99 → add {“Software Engineer”, “Java Developer”, “Python Developer”} 121 → add {“Software Engineer”, “Java Developer”, “Python Developer”}
  33. 33. {“Software Engineer”, “Java Developer”, “Python Developer”} {99, 121} 99 → {“Software Engineer”, “Java Developer”, “Python Developer”}
  34. 34. → {99, 131} {“Software Engineer”, “Java Developer”, “Python Developer”}
  35. 35. → → → → → →
  36. 36. ● ●
  37. 37. 1. http://go.indeed.com/docservice
  38. 38.
  39. 39.
  40. 40. → → →
  41. 41. ● ● ●
  42. 42. Engineering blog & talks http://indeed.tech Open Source http://opensource.indeedeng.io Careers http://indeed.jobs Twitter @IndeedEng

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