[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160130-en":3,"doc-seo-160130-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},160130,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Modeling Career Path Trajectories - Topic Sequence Model for Resume Job Transitions","Understanding how job markets work supports both individual job hunters and policy makers. This paper analyzes resume text and job transitions to model latent skills and career dynamics. A statistical topic model discovers topical components and skill co-occurrence, but underperforms for predicting next jobs. A topical sequence model trained on topic features improves prediction of subsequent job titles and learns hidden states with meaningful topic-transition probabilities.","Modeling Career Path Trajectories  \nDavid Mimno and Andrew McCallum Department of Computer Science University of Massachusetts, Amherst Amherst, MA fmimno,[mccallum](mccallumg@cs. umass. edu)[g](mccallumg@cs. umass. edu)[@cs. umass. edu](mccallumg@cs. umass. edu)  \nJanuary 19, 2008  \nAbstract  \nUnderstanding the structure and dynamics of the job market is important both from the local perspective of individual job hunters and from the global perspective of economists and policy makers. In this paper, we explore such questions by analyzing the text of a corpus of resumes and their job transitions. We 􀀌rst demonstrate the use of a statistical topic model to discover the latent skills that make up each job description and to map the cooccurrence patters of various skills. Although these topical features alone are good at discovering the structure of the job market, they are relatively poor at predicting job transitions. We next present a topical sequence model trained on topic features that has improved ability to predict subsequent job titles.  \n1 Introduction  \nUnderstanding the structure and dynamics of the job market is important from a variety of perspectives. Individuals are clearly very much concerned with establishing good career paths. Knowing what skills and combinations of skills are valued in various positions is very valuable. Understanding how to plan a career and seek positions that will lead to desirable career outcomes is another vital capacity. Institutions and policy makers should also understand patterns and trends in the job market in order to set policy and focus training resources where they can be most e􀀋ective.  \nThis paper presents the problem of career path modeling: predicting subsequent positions given previous work experience. We present a topical  \nsequence model that constructs a low-dimensional representation of the job market and learns a hidden state model that predicts job transitions.  \nOur 􀀌rst goal is to provide a tool that can analyze the static structure of jobs within businesses. Speci􀀌cally, we are interested in learning about the duties, responsibilities, and technical skills that make up jobs. We are also interested in learning in what ways people interact within organizations. Our second goal is to examine career paths over time. Having a model of the probability of various career path transitions could, for example, support a career counseling application that would 􀀌nd job opportunities that maximize the probability of some stated career goal.  \nThe data for this study consists of 9722 resumes. Each resume contains some number of records describing previous work experience. There are 54,549 such records in total, containing 2,383,402 words after the removal of stopwords.  \nEach job description is labeled with a job title. These titles are useful, but extremely noisy. The 􀀌rst problem is that there is little standardization in job titles, so they tend to be very sparse, exhibiting the common \\long tail\" phenomenon. Of the 28,828 distinct job titles in the corpus after lower casing and removing non-letter characters, only 536 or 1.9% appear 10 or more times. In contrast, 85% of distinct job titles appear only once. Additionally, only 33.5% of job descriptions have one of the frequent titles that appear 10 or more times. The second problem is that frequent job titles are often vague. For example, the most common job title is Consultant, a title that can cover a wide range of duties.  \nWe propose a topical sequence model, which learns hidden states that comprise career path trajectories based on a low dimensional representation of the components of job description text produced by an o􀀋-the-shelf topic model. We evaluate several models for predicting subsequent job titles. The topical sequence model produces better predictive likelihood than topics alone and the previous job title. In addition, the topical sequence model discovers coherent hidden states that have meaningful topic transi","cbCais0Su5bIh6kE","https://ap.wps.com/l/cbCais0Su5bIh6kE","pdf",197633,1,13,"English","en",105,"# Introduction\n## Career path modeling problem\n## Data description and job-title noise\n## Goals and applications\n# Topical Components of Resumes\n## Topic-model dimensionality reduction\n## Latent Dirichlet allocation topics","[{\"question\":\"What does the paper aim to model in career path trajectories?\",\"answer\":\"It models career path trajectories by predicting subsequent positions from previous work experience described in resumes.\"},{\"question\":\"How does the topic model contribute to the analysis?\",\"answer\":\"A statistical topic model learns latent topical components of job descriptions and maps co-occurrence patterns of skills.\"},{\"question\":\"Why does a topical sequence model improve over topics alone?\",\"answer\":\"Topics alone capture market structure but predict job transitions poorly; the topical sequence model better predicts subsequent job titles by learning hidden states and transition probabilities.\"}]","Modeling Career Path Trajectories - Topic Sequence Model for Resume Job Transitions | PDF",1788050717,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"modeling-career-path-trajectories-topic-sequence-model-for-resume-job-transitions","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/modeling-career-path-trajectories-topic-sequence-model-for-resume-job-transitions/160130/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-30",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the paper aim to model in career path trajectories?","Question",{"text":75,"@type":76},"It models career path trajectories by predicting subsequent positions from previous work experience described in resumes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the topic model contribute to the analysis?",{"text":80,"@type":76},"A statistical topic model learns latent topical components of job descriptions and maps co-occurrence patterns of skills.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does a topical sequence model improve over topics alone?",{"text":84,"@type":76},"Topics alone capture market structure but predict job transitions poorly; 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