Candidate Bot

Scrape

Preprocess

Structured data

Extract City, province, source, apply context

Text data

TF-IDF Vectorization

[0.14, 0, 0, 0.34, 0, 0, 0.33, 0, 0.21, ...]

PCA Dimensionality Reduction

[0.91, 0.03, 0.44, 0.82]
[0, 0, 0, 1, 1, 0, 1, 0.91, 0.03, 0.44, 0.82, 0.33, 0.10, 0.22, 0.81, 0.72, 0.43, 0.80 , 0.01]

Train / Predict

Train with existing
candidates

Predict with new candidates

Classification

Reinforcement Learning

Past data

resume

cover letter

Hired / Not Hired

Future data

resume

cover letter

Hired / Not Hired

1. Observe state

  • Who works here
  • Who is applying

2. Predict

  • What long term affect would each action have on the company

4. Remember

  • Record initial state, resulting stat, and immediate affect on the company

3. Act

  • hire / fire

5. Train

  • Pick samples from memory and train AI to better predict long term affect of the action taken
Initial State Action Result State Immediate Reward
S1 A1 S2 + $1000
S2 A2 S3 - $520

S1

{
    A1: "+$1,111,111", 
    A2: "-$222,222", 
    A3: "$3"
}

Long term reward

longTermReward_{A1} = immedateReward1 + 0.9(immedateReward2 + 0.9(immedateReward3 + ... ))
longTermRewardA1=immedateReward1+0.9(immedateReward2+0.9(immedateReward3+...))longTermReward_{A1} = immedateReward1 + 0.9(immedateReward2 + 0.9(immedateReward3 + ... ))
longTermReward_{A1} = immedateReward1 + 0.9(longTermReward_{A2})
longTermRewardA1=immedateReward1+0.9(longTermRewardA2)longTermReward_{A1} = immedateReward1 + 0.9(longTermReward_{A2})
//memory = {state1: S1, action: A1, state2: S2, reward: 1000}

input = [S1]
output = bot.predict(S1) //{A1: 1111111, A2: -222222, A3: 3}

output.A1 = 1000 + 0.9 * max(bot.predict(S2))


bot.train(input, output)

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By Rob McDiarmid

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