word2vec-recommender

by manasRK

Recommendation engine based on contextual word embeddings

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word2vec-recommender

GitHub license

Talk Submission at Pycon India 2016

Index

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 https://s3.amazonaws.com/iasnlp-models/output_models.tar
  • 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: http://jmcauley.ucsd.edu/data/amazon/

Installation

What is there inside the box?

| File | Function | |:---------------------:|:-------------------------:| | semsim_train.py | Main file to train models | | preProcessing.py | Methods to preprocess and clean data before feeding for training | | loadReviewModel.py | For loading review model | | loadRedis.py | For loading redis model |
| loadMetaModel.py | For loading meta model |

contributors

| 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.

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