Understanding Job-Skill Relationships Using Big Data and Neural Networks

Understanding Job-Skill Relationships using Big Data and Neural Networks

Abhinav Maurya Carnegie Mellon University Pittsburgh, PA – 15213 [email protected]

ABSTRACT Our proposed model is inspired by topic modeling where Nonlinear vector space embeddings have shown great promise latent topics occur throughout the documents of a corpus in many applications such as similarity search, analogy map- in varying proportions, which enables their identification to pings, dataset visualization, etc. In this paper, we propose characterize the text corpus. Similarly, in our model, vari- a simple distribution-to-distribution regression model based ation in the co-occurrence of jobs and skills can be used on neural networks which provides (i) job2vec: interpretable to disentangle which skills are associated with which jobs. embeddings of jobs in terms of associated skills, (ii) skill2vec: However, unlike unsupervised topic models such as LDA [1], converse embeddings of skills in terms of the jobs that re- we cast our problem of identifying job-skill associations as quire them, and (iii) SkillRank: a mechanism for ranking a supervised distribution-to-distribution regression, where skills associated with each job that can be recommended the input is the empirical distribution over job titles and to members aspiring for that job. Due to the simplicity of the output is the corresponding empirical distribution over our model, it has no hyperparameters that need tuning ex- skills for that person. Moreover, unlike unsupervised topic cept for the number of stochastic gradient descent iterations models such as LDA and supervised variants such as [4], our which is easily determined using the early stopping criterion. neural network model allows greater flexibility in job-skill We dicsuss preliminary results and insights from our model association formulation. Our results show that most job- using data from a major professional social network. skill associations are negative, which is intuitive since only a small subset of all job-skill pairs are useful in the real world. Topic models often allow only a non-negative modeling of Keywords association which would be restrictive in this application. Machine Learning, Neural Networks, Labor Market, Job- Skill Ranking, Vector Space Embeddings In the same vein as [5], our method provides embeddings for jobs and skills, known as job2vec and skill2vec respectively. However, the model used in [5] is quite different from ours, 1. INTRODUCTION since it is geared towards identifying word synonyms and The exchange of skilled services forms the driving force of analogies. A more complete version of this work will seek modern economies. We spend the formative years of our to distinguish between the predictive and semantic power of lives acquiring skills that prepare us for our desired careers, embeddings obtained from the method proposed here and and continue to acquire new skills throughout our profes- an adaptation of the word2vec model investiagted by [5]. sional careers. However, the decision of skill acquisition for a desired career is often fraught with uncertainty due to lack [3] matches members and jobs to make skill recommenda- of information. It is therefore important to study the associ- tions based on the demand of skills in jobs. However, it ation between jobs and the skills most commonly associated does not consider a user’s choice of career in making skill with these jobs. recommendations. In this paper, we identify specific skills for each job that a user can acquire if she aspires to get the The study of job-skill association has been challenging for job. two reasons: (i) lack of large-scale data on jobs held and skills acquired by members of the workforce, and (ii) the Our work bears similarity to Canonical Correlation Analysis multiple attribution problem which prevents us from associ- [2] in that it would be possible to map job and skill vectors of ating the acquisition of a particular skill with a specific job a user such that the distance between the mappings is mini- held by a person. With the advent of online professional mized in the projected space. However, CCA would lose the social networks, large-scale data is now available to study benefit of using an asymmetric loss such as KL Divergence the relationship between jobs and skills. In this paper, we which allows us to model only the present skills. tackle the second problem of multiple attribution in job-skill association by exploiting naturally occurring variation in the sets of jobs and skills across large-scale labor market data. 2. DATA Our dataset consists of online profiles crawled from a major worldwide professional social network. Each profile is an- notated with a city and an industry. We focus on a subset of the entire data from a diverse set of cities and industries Bloomberg Data for Good Exchange Conference. 16-Sep-2018, New York City, NY, USA. Industry Most Frequent Positions Computer Software Software Engineer Hospital and Health Care Intern Financial Services Research Assistant Real Estate Director Research Manager Marketing and Advertising Vice President Cities Consultant San Francisco Bay Area Account Executive New York Metropolitan Area Project Manager Seattle Owner Pittsburgh Most Frequent Skills Mumbai Microsoft Office New Delhi Management Microsoft Excel Marketing Table 1: Industries and cities included in the evaluation. Customer Service Research Leadership Social Media listed in table1 for the purpose of our preliminary analysis. PowerPoint From each profile, we extract and use the set of job titles Sales held by the person as well as the set of skills the person has listed on the profile. Summary statistics about the used data are provided in table2. Datapoints are divided into Table 3: Most frequent job titles and skills in the data. 90% training, 5% validation, and 5% test data. We identi- fied 12,652 unique job titles and 15,185 unique skills from our data. Most frequent job titles and skills are listed in of skills. This is a major reason why we choose the KL table3. Divergence loss in our model optimization. In the case of a single categorical output, the KL Divergence loss simpli- Statistic Value fies to the popular cross-entropy loss widely used in neural Number of datapoints 157252 network classifiers. Number of train datapoints 125801 Number of validation datapoints 15725 1 Number of test datapoints 15726 The model was implemented in PyTorch . It is notable Number of unique skills 15185 that the number of training epochs is the only parameter Number of unique titles 12652 we need to tune in our model, and it needs a single training run to find the right number of iterations to avoid overfitting. There are no other model-specific parameters. This is Table 2: Dataset Summary Statistics in contrast to hyperparameter-heavy models that may need expensive grid search, random search, or Bayesian optimiza- 3. METHODOLOGY tion to tune the hyperparameters. Since we have over 12 thousand unique job titles and over 15 thousand unique skills in our data, the challenge lies in effec- tively formulating a neural network model that can learn to perform a high-dimensional distribution-to-distribution re- Softmax gression by ingesting vast amount of examples available in Parameter � x our data. Our simple neural network model is designed to Skills perform this high-dimensional map from a distribution over SkillScores

positions to a distribution over skills. Loss Divergence KL

Jobs InputDistribution Jobs over The architecture of our SkillRank neural network model is InputDistribution Skills over shown in ﬁgure1. The model maps an empirical distribu- Skills over Distribution Predicted tion over job titles to an empirical distribution over skills: Figure 1: Our Neural Distribution-to-Distribution Regres- f : J → S. Given an empirical distribution over jobs j ∈ J , sion Model. the model ﬁrst performs a linear transformation φ · j, where SxJ φ ∈ R , S is the number of skills and J is the number of job titles. The result of the linear transformation is a 3.1 Embeddings vector of unnormalized real-valued skill scores. The model SxJ We recall that φ ∈ R , S is the number of skills and J is then applies a softmax transformation to normalize the skill the number of job titles. Thus, the rows of φ correspond to scores and output a distribution over skills. The model is skills and columns to jobs. The matrix φ encodes the asso- trained by minimizing the KL Divergence loss between the ciations between jobs and skills. A positive value indicates groundtruth empirical distribution over skills and the pre- that a skill is desired by a job, and a negative value indicates dicted distribution over skills. that it is highly unlikely for the corresponding position-skill combination to occur in the data. Since the KL Divergence is asymmetric, it does not penalize any absent skills in the groundtruth empirical distribution 1http://pytorch.org/ ing 2-dimensional t-SNE visualization for skill embeddings. 7 Train Loss Validation Loss Not surprisingly, related skills are often located close to each Test Loss 6 other in the embedding vector space. For example, “commercial real estate,”“investment properties,”“tenant reten- 5 tion,” etc. are located close to each other.

4 From ﬁgure4, we see the parameter matrix φ visualized be-

KL Divergence Loss fore and after spectral coclustering. The existence of bands 3 in ﬁgure 4b indicates that there is the possibility of cocluster-

2 ing or mixed-membership stochastic blockmodel structure on parameter φ. This can help in identiﬁcation of closely- 0 100 200 300 400 500 SGD Iteration related job and skill bundles.

Figure 2: Training, Validation, and Test Losses. Figure5 shows two histograms. The first one shows the number of skills on the X-axis and the number of positions that were positively associated with a certain number of skills on job2vec: To calculate the embedding of a job, we pass the Y-axis. Here, positive association means a positive value its corresponding column through a softmax transformation. for the corresponding φ entry. Similarly, the second figure The softmax transformation of a vector exponentiates its en- shows the number of jobs versus the number of skills posi- tries and normalizes the exponentiated entries so that they tively associated with a particular number of jobs. The fig- sum to 1. Thus, each job’s embedding indicates its distri- ure illustrates that there is considerable diversity and speci- bution over skills. The exponentiation is also useful to get ficity in job-skill associations, since most jobs are associated meaningful job embedding vectors, since it diminishes the with a moderate number of skills and most skills are asso- negative values drastically and therefore represents a job ciated with a moderate number of jobs, as indicated by the using the skills its positively associated with. rapid tapering of the histograms to the right of the X-axis. skill2vec: Similar to job2vec, skills can also be embedded 1e7 to get a useful representation. Skills correspond to rows of the φ matrix. Therefore, their embeddings are obtained by 3.0 applying the softmax transformation to each row of φ. 2.5

2.0 4. PRELIMINARY RESULTS In our implementation, we used minibatch gradient descent 1.5 with a minibatch size of 1000. The training and validation Parameter Count 1.0 loss curves during the optimization procedure and the final test loss are shown in figure2. Since the validation loss 0.5 starts rising after epoch 122 (shown with a vertical red line 0.0 8 6 4 2 0 2 in the figure), we choose the model obtained at the end Parameter Magnitude of 122 iterations as our final model as per validation-based early stopping criterion to prevent overfitting. Figure 3: Histogram of values in φ.

Figure3 shows the histogram of values from the parameter matrix φ. Most values in the matrix are negative. This 6 6 makes intuitive sense because most job-skill pairs are not 4 4 naturally associated with each other. Only 1.13% values 2 2 in the matrix are positive indicating only a small subset of 0 0 job-skills are positively associated with each other. 2 2 Skills Skills

4 4 In table4, we show the job-skill pairs which have the highest 6 6 corresponding φ values. It is interesting to note the job-skill 8 8 associations are non-trivial i.e. it’s not the most common 10 10 skills such as “Microsoft Oﬃce” that are repeatedly recom- Jobs Jobs mended for every position. Instead, each skill recommended (a) Parameter φ (b) After Spectral Coclustering for the job is precisely relevant for the job. Figure 4: Fig (a): Parameter matrix φ visualized. Fig (b): Figure6 shows a 2-dimensional t-SNE visualization of job Parameter matrix φ visualized after spectral coclustering embeddings. Job titles that are tightly clustered in the vi- with 100 coclusters. sualization are often related to each other. For example, “postdoctoral scholar” and “postdoctoral fellow” are situated very close to each. A number of medical industry jobs such 5. CONCLUSIONS as “medical director,”“consultant physiotherapist,”“clinical In this paper, we presented a simple neural approach to trial manager,” “pharmacy manager,” etc. are also located identifying job-skill associations from large-scale labor mar- closely in the visualization. Figure7 shows a correspond- ket data. The model helps us rank skills by the order of Job Skill 250 Associate Media Director Mobile Advertising 500 200 Account Director Integrated Marketing 400

150 Management Supervisor Interactive Advertising 300

Clinical Dietitian Medical Nutrition Therapy 100 200 Number of Skills Product Management Go-to market Strategy Number of Positions Senior Art Director Corporate Identity 50 100 0 0 Chief Compliance Officer Securities Regulation 0 50 100 150 200 250 300 100 150 200 250 300 Speech Language Pathologist Articulation Number of Skills Number of Positions Senior Art Director Typography (a) Histogram of number of (b) Histogram of number of po- Account Director Creative Direction skills per position. sitions per skill. Sr. Product Manager Go-to market Strategy Art Director Logo Design Figure 5: Fig (a): Number of skills versus number of posi- Freelance Art Director Typography Associate Creative Director Concept Development tions positively associated with a certain number of skills. Speech Language Pathologist Speech Therapy Fig (b): Number of positions versus number of skills posi- iOS Developer Swift tively associated in φ with a certain number of positions. Copywriter Creative Writing Management Supervisor Relationship Marketing Law Clerk Legal Research 6. REFERENCES Enterprise Account Manager Solution Selling iOS Developer Xcode [1] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Sr. Product Manager Product Launch dirichlet allocation. Journal of machine Learning Social Work Intern Crisis Intervention research, 3(Jan):993–1022, 2003. Group Creative Director Art Direction [2] D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor. Financial Advisor Retirement Planning Canonical correlation analysis: An overview with Media Supervisor Mobile Marketing Law Clerk Legal Writing application to learning methods. Neural computation, Software Development Manager Scalability 16(12):2639–2664, 2004. Associate Creative Director Interactive Advertising [3] A. Maurya and R. Telang. Bayesian multi-view models Mortgage Consultant FHA for member-job matching and personalized skill Account Director Brand Development recommendations. In IEEE International Conference Graphic Designer Corel Draw on Big Data (Big Data), 2017, 2017. Consultant Physiotherapist Physical Therapy Editorial Intern Journalism [4] J. D. Mcauliﬀe and D. M. Blei. Supervised topic Creative Director Logo Design models. In Advances in neural information processing Account Director Customer Insight systems, pages 121–128, 2008. Graphic Designer InDesign [5] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Editorial Intern Proofreading QA Engineer Selenium Eﬃcient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.

Table 4: Examples of most positively associated job-skill pairs. their importance to each position, and allows us to embed jobs and skills using job2vec and skill2vec to enable tasks such as similarity searches and ontology visualization. Postdoctoral Fellow Postdoctoral Scholar SeniorScientist Research II Technician 75

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