基于上下文词嵌入的推荐引擎_Python_JavaScript_下载.zip

# word2vec-recommender [](https://img.shields.io/pypi/l/pyzipcode-cli.svg) [Talk Submission at Pycon India 2016](https://in.pycon.org/cfp/2016/proposals/creating-a-recommendation-engine-based-on-contextual-word-embeddings~aOZGe/) ## Index - [What it is ??](#what-it-is?) - [How it is done?](#how-it-is-done?) - [Technologies used](#technologies-used) - [Data and Models](#data-and-models) - [Installation](#installation) - [What is there inside the box :package: ?](#What-is-there-inside-the-box?) - [Contributors](#contributors) - [Issues :bug:](#issues) ## 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 * [Lets do it !!](https://github.com/manasRK/word2vec-recommender/blob/master/Python%20Cloud%20Setup.md) ## 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](https://github.com/manasRK) | | Akhil Gupta | Intern @ Amazon | [@github](https://github.com/codeorbit) | | Vinay Kumar | MS @ IIT-KGP | [@github](https://github.com/vinay2k2) | ## Issues :bug: You can tweet to [Manas Ranjan Kar](https://twitter.com/manasrnkar) or [Akhil Gupta](https://twitter.com/decoding_life) if you can't get it to work. In fact, you should tweet us anyway.