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Job word embeddings

This project builds word embeddings from job postings using Gensim's word2vec API.
The goal of this project is to experiment word2vec with job postings as well as applying the trained model in demo application.

Index

Jupyter Notebook

This repository contains jupyter notebook that explains the details of this project.

jupyter notebook Job_word_embeddings.ipynb

Experiment results

Similar words

You can find smilar job titles with a target job title, which will be useful when you find jobs with your current job title.

word2vec_model.most_similar('Webエンジニア')
=> [('サーバーサイドエンジニア', 0.8966489434242249),
 ('webアプリケーションエンジニア', 0.8889687061309814),
 ('開発エンジニア', 0.8835119009017944),
 ('フルスタックエンジニア', 0.8723335266113281),
 ('rubyエンジニア', 0.8664447069168091),
 ('サーバサイドエンジニア', 0.8570215702056885),
 ('web開発エンジニア', 0.8520206212997437),
 ('アプリケーションエンジニア', 0.8453050255775452),
 ('バックエンドエンジニア', 0.8452906608581543),
 ('railsエンジニア', 0.8418977856636047)]

You can also find similar skills. this will be also useful when you find jobs which require similar skills with your skill set.

word2vec_model.most_similar('Ruby')
=> [('php', 0.9285846948623657),
 ('perl', 0.8777764439582825),
 ('java', 0.8664125800132751),
 ('ruby on rails', 0.8557538986206055),
 ('python', 0.8433256149291992),
 ('elixir', 0.8411149978637695),
 ('symfony2', 0.8331097364425659),
 ('言語', 0.8303745985031128),
 ('web エンジニア', 0.8260266780853271),
 ('clojure', 0.8259769082069397)]

You can also give multiple skills to the trined model. those skills can be your current skill set.
And then you will get similar job titles and skills with yourself.

postive_skills = ['webエンジニア', 'ruby', 'redis', 'postgresql', '機械学習', 'deep learning', 'html', 'css', 'javascript']
word2vec_model.most_similar(positive=postive_skills)

=> [('webフロントエンドエンジニア', 0.8989949226379395),
  ('elixir', 0.8973215222358704),
  ('ll言語', 0.8954563736915588),
  ('web・アプリエンジニア', 0.8946321606636047),
  ('script', 0.894004225730896),
  ('web エンジニア', 0.893429696559906),
  ('phoenix', 0.8932678699493408),
  ('プロダクト開発エンジニア', 0.8929529190063477),
  ('スクレイピング', 0.8926692008972168),
  ('webサービス開発エンジニア', 0.8926246166229248)]

You can spacify negative skills. And you will get job titles and skills which may be intesteding to you.

postive_skills = ['webエンジニア', 'ruby', 'redis', 'postgresql', '機械学習', 'deep learning']
negative_skills = ['html', 'css', 'javascript']
word2vec_model.most_similar(positive=postive_skills, negative=negative_skills)

=> [('数理モデル', 0.7106159329414368),
  ('chainer', 0.6900843381881714),
  ('lucene', 0.689948320388794),
  ('音声合成', 0.6863459348678589),
  ('hbase', 0.6846038699150085),
  ('python', 0.6761983036994934),
  ('hive', 0.6735614538192749),
  ('データセット', 0.6731375455856323),
  ('分散データベース', 0.6727677583694458),
  ('erlang', 0.6702989935874939)]

Visualization

Semantic job search

You can develop semantic job search using the similar words as synonyms in Elasticsearch.

Generate synonym file

First, you need to generate sysnonym file by running following code.

# save synonyms to synonym.txt
python train_word2vec_model.py
python generate_synonym_file.py

This is sample lines of the synonym file

...
機械学習=>機械 学習,自然 言語 処理,データ マイニング,マイニング,統計 学
データサイエンティスト=>データ サイエンティスト,データ サイエンス,データ マイニング,データ アナリスト,機械 学習
機械学習エンジニア=>機械 学習 エンジニア,エンジニア,開発 エンジニア,データ 分析 エンジニア,線形 代数,データ エンジニア
...

Create Elasticsearch index

You need to create ES index and set custom mapping to use synonym token filter at query timing.

For demonstration, I prepared 2 mappings. One is for normal search and the other is for semantic search. (these mappings are valid for elasticsearch v6.2.2).

# apply normal search mapping to the index "job_postings"
curl -XPUT -H "Content-Type: application/json" \
  localhost:9200/job_postings -d @jp_mapping.json

# apply semantic search mapping to the index "job_postings_semantic"
curl -XPUT -H "Content-Type: application/json" \
  localhost:9200/job_postings_semantic -d @jp_semantic_mapping.json

Insert job postings

# insert job postings to the normal serarch index
python insert_jobs_to_es.py job_postings

# insert job postings to the semantic serarch index
python insert_jobs_to_es.py job_postings_semantic

Result

Let's compare the results of those 2 settings.

Normal Search Semantic

As you can see, you get more results from the semantic search because it finds job postings that contain similar words, such as 機械学習, 統計的 for データサイエンティスト in the above example.