Raw Data Analysis Par/cipants Your text would go here. A Personalized Company Recommender System for Job Seekers Ruixi Lin, Yue Kang, Yixin Cai Algorithm F1 Score Decision Tree 50.94% Naive Bayes 58.49% linear SVM(1-v-rest) 63.52% linear SVM(1-v-1) 62.26% linear SVM(ecoc) 63.52% Neural Network 66.04% Confusion Matrix test set Company Precision Recall F1 score Google 56.25% 84.91% 67.67% Facebook 80.00% 37.74% 51.28% Apple 74.07% 75.47% 74.77% train set Company Precision Recall F1 score Google 56.19% 72.67% 63.37% Facebook 77.68% 58.00% 66.41% Apple 64.58% 62.00% 63.27% Results >=1 Year(s) of Experience >=5 Year(s) of Experience >=10 Year(s) of Experience >=1 Year(s) in Current Company >=5 Year(s) in Current Company Has Doctorate Degree Has Masters Degree Is bilingual Has PublicaGons or Patents >= 20 Number of Skills Google Intern Facebook Intern Apple Intern Gender 59.50% 60.00% 60.50% 61.00% 61.50% 62.00% 62.50% 63.00% 63.50% 64.00% 64.50% 65.00% 20 30 50 70 50.00% 52.00% 54.00% 56.00% 58.00% 60.00% 62.00% 64.00% Confusion Matrix: • Google has low precision and high recall • Diverse employee body • Likely to classify everyone to Google • Facebook has high precision and low recall • Unlikely to classify other employees to Facebook • Likely to classify Facebook to other companies Feature Importance • Apple • Experienced employees • More long Gme employees • More skills • Google • More new employees recently • Master degree • Bilingual • Facebook • More new bloods in the past 5 years • Internship experience Contact info Result Change on Excluding One Feature Eﬀect of Number of IteraGon on Neural Network Results F1 Score on Diﬀerent Algorithms [email protected] [email protected] [email protected]

APersonalizedCompanyRecommenderSystemforJobSeekerscs229.stanford.edu/proj2015/221_poster.pdfRaw$Data$ Analysis Par/cipants Your%textwould%go%here.%% APersonalized"Company"Recommender"System"for"Job"Seekers

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Raw Data

Analysis

Par/cipants Your text would go here.

A Personalized Company Recommender System for Job Seekers Ruixi Lin, Yue Kang, Yixin Cai

Algorithm F1 Score Decision Tree 50.94% Naive Bayes 58.49%

linear SVM(1-v-rest) 63.52% linear SVM(1-v-1) 62.26% linear SVM(ecoc) 63.52% Neural Network 66.04%

Confusion Matrix test set

Company Precision Recall F1 score Google 56.25% 84.91% 67.67%

Facebook 80.00% 37.74% 51.28% Apple 74.07% 75.47% 74.77%

train set Company Precision Recall F1 score Google 56.19% 72.67% 63.37%

Facebook 77.68% 58.00% 66.41% Apple 64.58% 62.00% 63.27%

Results v >=1 Year(s) of Experience v >=5 Year(s) of Experience v >=10 Year(s) of Experience v >=1 Year(s) in Current Company

v >=5 Year(s) in Current Company

v Has Doctorate Degree v Has Masters Degree v Is bilingual v Has PublicaGons or Patents v >= 20 Number of Skills v Google Intern v Facebook Intern v Apple Intern v Gender

59.50%

60.00%

60.50%

61.00%

61.50%

62.00%

62.50%

63.00%

63.50%

64.00%

64.50%

65.00%