The document discusses job recommendations and similarities between users on a job site. It notes that millions of new jobs are discovered daily and 1.5 million new users visit the site each day. It also explains that the average lifespan of a job posting is about 30 days. The document then describes algorithms used to calculate similarities between users based on jobs viewed, and how job recommendations are personalized based on clustering users with similar interests.

3. I

16. Job recommendations are significantly different
Rapid inventory growth - Millions of new jobs discovered every day

17. Job recommendations are significantly different
Rapid inventory growth - Millions of new jobs discovered every day
~ 1.5 million new users visit indeed every day

18. 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

19. 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

37. Compute similarity
For ui
In {Users}
For uj
In {Users}
SIMi,j
= compute_similarity(ui,
uj
)

47. →
→

59. →

61. ∩ ∪

63. Items[Ui
] = {x1
, x2
, ..xn
}
H
minhashH
(Ui
)= min{ x∈Itemsi
| H(x) }

65. Similarity(U1, U2) = 1, if minhash(U1) == minhash(U2)
Similarity(U1, U2) = 0, otherwise
This is an unbiased estimator

69. Similarity(U1, U2) = 1, if minhash(U1) == minhash(U2)
Similarity(U1, U2) = 0,

71. Hk
Hk
Prob(minhashH
(Ui
) == minhashH
(Uj
)) = J(Ui
, Uj
)

74. user → {job1, job2, job3,..}

75. H = {H1
, H2
, ..H20
}
for user in Users
for hash in H
minhash[hash] = min{x∈Itemsi
| hash(x)}

76. For ui
In {Users}
For uj
In {Users}
SIMi,j
= compute_similarity(ui,
uj
)

77. user1 → (111, 123, 134, 148, ..129)
user2 → (101, 123, 139, 148, ..135)
user3 → (191, 103, 126, 108, ..119)
user4 → (191, 103, 126, 108, ..129)
...

78. user → {cluster}
cluster → {users}

79. 123 → (user1, user2)
148 → (user1, user2)
129 → (user1, user4)
191 → (user3, user4)
...

80. →

81. →
user1 → {job1, job2}
user2 → {job2, job3, job5}
123 → {user1, user2}

82. →
user1 → {job1, job2}
user2 → {job2, job3, job5}
123 → {job1, job2, job3, job5}

83. 1. user → {cluster}

84. user → {cluster} user1 → {111, 123, ..}

85. 111 → {job5, job2, job9}
123 → {job1, job2, job3, job5}
{job2, job5, job9, job1, job3}
→
→

86. {job2, job5, job9, job1, job3}

91. 1.

92. →
→ {101, 121}

93. →
→ {101, 121}
{“Software Engineer”,
“Java Developer”, “Python Developer”}

94. →
→ {101, 121}
{“Software Engineer”,
“Java Developer”, “Python Developer”}
minhash({“Software Engineer”, “Java Developer”,
“Python Developer”}) → {99, 135}

95. →
→ {101, 121}
{“Software Engineer”,
“Java Developer”, “Python Developer”}
minhash({“Software Engineer”, “Java Developer”,
“Python Developer”}) → {99, 135}
→ {99, 121}

97. →
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”}

98. {“Software Engineer”, “Java Developer”,
“Python Developer”} {99, 121}
99 → {“Software Engineer”, “Java Developer”, “Python
Developer”}

99. → {99, 131}
{“Software Engineer”, “Java Developer”,
“Python Developer”}

114. →
→
→
→
→
→

116. ●
●

119. 1. http://go.indeed.com/docservice

123. →

131. →

134. →
→
→

149. ●
●
●

150. Engineering blog & talks http://indeed.tech
Open Source http://opensource.indeedeng.io
Careers http://indeed.jobs
Twitter @IndeedEng