Simple book recommender

Project discovering creative approaches to recommender systems.

See presentation for evaluation and practical info: simple_book_recommender.pdf.

Using Book-Crossing Dataset.

Setup

1. Clone the repo [email protected]:tomas2211/book_recommender.git
2. Install requrements pip install -r requirements.txt
3. Download and unzip the dataset & trained models ./download_data.sh
4. (Not required - only when training node2vec model git submodule init && git submodule update)

Demo

For a quick demo, run:

python eval_qualitative.py --interactive --format

The four tested models will be loaded, and you will be able to enter queries (book names).

API

The kNN model is reachable through a simple API.

SwaggerHub documentation.

TLDR: https://abiding-ripple-272918.ew.r.appspot.com/query?name=book-name

Protip: pass 'format' parameter for a human-readable response: https://abiding-ripple-272918.ew.r.appspot.com/query?format=1&name=book-name