sytemize the process of retraining/saving recommenders

The serialized recommenders will need to be periodically retrained- for example as we update python packages, add new experiment configurations, or update what information the serialized recs contain. We want to be able to easily rebuild the files and update github.

- [ ] Add command line option to ai.py that just trains and saves recommenders
- [ ] Add instructions/utility script that documents how to retrain the SVD recommenders for web and PennAIpy experiment configurations
- [ ] If feasible, create github or jenkins action to regenerate the files in a new branch and create a pr (possible complication is git lfs)

Start by reading ai.py and locating how serialized recommenders are currently trained and saved. Check the SVD recommenders for the web and PennAIpy experiment configurations, then document a repeatable retraining and GitHub update process. Assess the Git LFS complication before considering GitHub or Jenkins automation that regenerates files and opens a pull request.

Written by the indexing model from the issue text.