We show how to create an embedding to predict product reviews, using TensorFlow machine learning framework and the Neo4j graph database. It achieves 98% validation accuracy. Introduction
A common problem in business is product recommendation. Given what a user has liked so far, what should we suggest they purchase next? Just as a waiter asking if you’d like another drink drives higher revenues, so does successful recommendations.
There are many approaches to recommendation. We’re going to focus on review prediction: given a product a person has not reviewed, what review would they give it? We can then recommend to that person the products we predict they will favorably review.
The data for this experiment can be generated by executing ./generate.py --dataset article_1 from our generate-data repository
Setup your environment:
pipenv install
pipenv shell
Then in the virtualenv:
python -m src.train
Finally, check out the results:
tensorboard --logdir ./output