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readme.txt
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readme.txt
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All scripts are designed to be run on Python 3.8.3 with the dependencies listed in requirements.txt, which can be installed via "pip install -r requirements.txt".
To start the recommender system (CACBCF), first make sure either pre-processed pickled user and business datasets (user.pkl & businesses.pkl),
or the entire Yelp Open Dataset in json form, are inside the same file directory as main.py.
Then run main.py (python main.py) to enter the main user interface.
If either user.pkl or businesses.pkl is not present then they will be prepared from scratch from the Yelp Dataset (~10 mins).
Then, if post-processed-user-data.pkl is not present, the user profiles and model will be trained from the above (~15 mins).
From this point onwards, any retraining (e.g. user feedback) will be take a second or 2.
For a list of sample users to login as, either inspect users.pkl contents or use one of the following supplied examples, or instead register yourself as a new user!
Sample user_ids:
3aYeG-x5A44GIgmBHrwyAA (227 reviews)
U1vl4SQzO3wTAWlYVnSjnw (163 reviews)
ZD76B53WiEdv3g2lNgTbNg (112 reviews)
sKVpHfhkG_Nvgf_Vfb91Cg (88 reviews)
CebjpVd3PsofCgotWp60pg (74 reviews)
5ca2MkCJFAMafDrRxLMlXQ (44 reviews)
ygjIo5gLQ8wmsOcTDiHG2Q (29 reviews)
1uUwbiQfayJiGhqx3CjjIg (11 reviews)
C5faJHojrEUqX5VMFrsz3Q (5 reviews)
For sample locations, either pick anywhere in Quebec or choose from any of the following examples:
Boulevard Saint-Laurent, Montreal
H9B 1P7
Brossard
Alternatively, run runMetrics.py (python runMetrics.py) to run metrics (WARNING: on the current dataset this may take a few hours).