Need help with word2vec-recommender?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

132 Stars 54 Forks MIT License 116 Commits 0 Opened issues


Recommendation engine based on contextual word embeddings

Services available


Need anything else?

Contributors list

# 329,682
68 commits
# 443,692
36 commits
# 673,371
1 commit


GitHub license

Talk Submission at Pycon India 2016


What it is?

How can we create a recommendation engine that is based both on user browsing history and product reviews? Can I create recommendations purely based on the 'intent' and 'context' of the search?

This talk will showcase how a recommendation engine can be built with user browser history and user-generated reviews using a state of the art technique - word2vec. We will create something that not only matches the existing recommender systems deployed by websites, but goes one step ahead - incorporating context to generate valid and innovative recommendations. The beauty of such a framework is that not only does it support online learning, but is also sensitive to minor changes in user tone and behavior.

How it is done?

The trick/secret sauce is - How do we account for the 'context' and build it in our systems? The talk will answer these questions and showcase effectiveness of such a recommender system. * ## First Milestone :tada: Subset of the engine's functionality was completed during a project undertaken at IASNLP 2016 held by Language Technology Research Center (LTRC), IIIT Hyderabad

Technologies used

  • Google's Word2vec
  • Gensim
  • Numpy
  • Flask, Redis.

Data and Models

  • Rest of the models (User & Metadata) can be downloaded from
  • Amazon review data will be made available (for research purposes) on request. Please contact Julian McAuley ([email protected]) to obtain a link. Sample data files available at:


What is there inside the box?

| File | Function | |:---------------------:|:-------------------------:| | | Main file to train models | | | Methods to preprocess and clean data before feeding for training | | | For loading review model | | | For loading redis model |
| | For loading meta model |


| Author | Working As | contact @| | ------------- |:-----------------------------------:| -----: | | Manas Ranjan kar | Practice Lead @ Juxt Smart Mandate |@github | | Akhil Gupta | Intern @ Amazon | @github | | Vinay Kumar | MS @ IIT-KGP | @github |

Issues :bug:

You can tweet to Manas Ranjan Kar or Akhil Gupta if you can't get it to work. In fact, you should tweet us anyway.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.