Information Retrieval and Extraction have wide ranging applications, from analyzing social media content and summarizing sports activities to other, more commercial applications like automation of customer service chats and search engines.We use our knowledge of the domain to solve this newly emerging problem of shortlisting candidates for a job. We provide the following functionalities: Extract important personal as well as professional details from the candidate resumes, presenting a more structured view of the provided information. We accept a ‘job description’, and then scan through the index of all the candidate resumes to dig out the 10 best candidates for the job. Besides, we also accept a resume, and present other resumes most similar to the given one. This helps if you know exactly what kind of person you need for the job. Moreover, we support ‘field queries’, whereby you may narrow down your search to incorporate specific details.

1. Job
Recommendation
Engine
Harshendra
Mahak Gambhir
Vishal Gupta

2. Introduction
● Most job applications are through resumes
● Short Listing resumes is a painful task
● Especially when there are thousands of them
● Manual checking is not feasible

3. Semantic Search
● What is not a semantic search ?
● Search based on keywords ?
Ex: grep “icpc”
● Can we make it better ?
○ Weightage to sections
○ To be able to search in a particular section
○ Recommend resumes similar to a particular resume
○ NLP vs Natural Language Processing

4. Challenges
● Structure of a resume
● Content Segregation - How well is it written ?
● Extraction of entities
○ Names
○ Marks/Grades
○ Previous Organizations
○ Years of experience
○ Skills
○ Projects
● Ranking among the shortlisted resumes

5. Problems addressed
● Creation of info box for each resume.
○ Name: XYZ
○ Email : abc@gmail.com
○ Phone: xx-xxxxxxxxx
○ Skills
○ Projects
● A search interface
○ Query> Machine Learning, ACM ICPC, Scikit Learn
● Recommend resumes similar to a given resume
○ Resume Path> /path/to/resume

6. The Process Flow
Resumes in
PDF format
PDF to text
Extract
features
Ranking
Model
Top K
Candidates
Resumes in
Text format
Preprocessing
Parsing Infoboxes
JSON files
& Indexes
Used to display
a brief summary
of the candidate.

7. Approach
● Each resume is a text document
● Extract the sections in each resume
● NER for names
● Index the resumes according to sections
● Extract features from each resume

8. Technical Details
● Named Entity Recognition for extracting names
○ Used Stanford NER
● Parse the sections using most common section names
○ Project/Projects/Academic Project/Major Projects/Minor Projects
○ Skills/Technical Skills/Technical Expertise and so on
● Stanford temporal tagger for extracting the temporal
information
○ Jan 2012 - Jan 2014 (2 years)
○ Useful in extracting the years of experience

9. Technical Details
● Bag of words approach
● Vector space model
● Extract feature for each resume
○ Boolean
■ A term is present or not d
○ Tf-Idf
■ Tf-idf weight of a term w.r.t the document d
● Rank the resumes based on distance functions
○ Cosine Similarity
○ Euclidean distance

10. Features
Let N be the |V|, d1 and d2 be two resumes
d1
: v1 <v11
v12
. . . v1N
>
d2
: v2 <v21
v22
. . . v2N
>
where,
vij
: Tf-Idf(di
,tj
)
tj
is the jth resume
Tf-Idf(d,t) = tft
x log(N/dft
)
tft
- Term frequency of term t in document d
dft
- document frequency of term t

11. Ranking
● Convert the test query/document into tf-idf feature
vector t
Ex: test = “acm icpc, machine learning”
● Calculate cosine similarity with each document and rank
them
cos-sim(d1
,t), cos-sim(d2
,t) and so on
● Rank the resumes in non-decreasing order
● Choose top K

12. Tools/Frameworks
● Language: python 2.7
● Frameworks:
○ Scikit-learn
○ Nltk
○ Stanford NER tagger
○ Stanford temporal tagger
● Intuition!

13. Demo

14. Links
● Code: https://github.com/gvishal/Sematic-Job-Recommendation-Engine
● Deliverables: https://www.dropbox.
com/home/IRE_Major_Project/deliverables