Been working more on building really generic and simple machine learning tools. This one is a generic recommendation script built to run as a lambda or gcf. This iteration on my GitHub is built to run locally but it’s straight forward enough to import json, parse, and run it as a microservice.
Requirements
- numpy
- gensim
- nltk==3.4.5
- textblob==0.15.3
Usage
Run locally, the recommender crawls through a column of a csv and matches the recommendations for similar content.
Those are based on the content passed in the –text field. Can use the –recs option to define the number of recs you’d like to recieve in response.
python recommender.py --file='my.csv' --text="Flash update" --column="title" --recs=10
Some of the uses I can see here would be scanning an existing website for similar content, building a model to find similar tasks, etc. Hope someone else finds it useful.