mihaineacsu/Recommandation-Engine
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Recommendation Engine Dependencies: MongoDB: https://www.mongodb.org/ PyMongo: https://github.com/mongodb/mongo-python-driver [see bellow] Project Primary Structure: -- data_processing Place to keep all the files related to data processing, from text parsing/filtering to the communication with the database. - mongo.py - clean wrapper for all operations involving the database -- recommendation_engine -- classic collaborative filtering techniques TODO: add implementation -- Collaborative Topic Modelling (Blei 2011) TODO: add implementation https://www.cs.princeton.edu/~chongw/papers/WangBlei2011.pdf -- eval Place to keep scripts for the engine's evaluation TODO: review evaluation methods TODO: add test results -- gui [Optional] Engine Graphic interface Tools for building user graphs, data plots and the like. Database Setup: To install mongodb: Download archive from: https://www.mongodb.org/downloads On Linux: Extract archive somewhere and it's ready to go. The interesting files are in ./bin: mongo (the console) - which you can use to inspect the database mongod (the actual database server) I suggest creating a sym-link on /usr/bin at least for these binaries. mongoimport, mongoexport and mongorestore are also useful. To start the mongo database: sudo mongod --dbpath [PATH_TO_WHERE_YOU_WANT_TO_STORE_THE_DATA] This process has to always run while you use the database. If you want to play a bit with the data, you can do so using the mongo console. eg: > use yelp > show collections Here "yelp" is the name of the database. Right now it's just an empty database, but we are about to add our dataset. Note: There are also some gui apps for mongo, I can't recommend any though. Dataset: Now, you need to get the dataset from: http://www.yelp.com/dataset_challenge You'll see it's an archive of json files, which happens to be a mongo-friendly format. Import them in the database is as easy as: mongoimport --db yelp --collection users yelp_academic_dataset_user.json mongoimport --db yelp --collection business yelp_academic_dataset_business.json mongoimport --db yelp --collection tip yelp_academic_dataset_tip.json mongoimport --db yelp --collection review yelp_academic_dataset_review.json Note: In the mongo console, I suggest setting up indexes for the fields you are most likely to use in your queries: e.g: > use yelp > db.users.createIndex({user_id:1}) > db.review.createIndex({user_id:1, review_id:1}) > db.business.createIndex({business_id:1} > db.tip.createIndex({user_id:1, business_id:1}) > db.checkin.createIndex({business_id:1}) More on how to use mongo: http://docs.mongodb.org/manual/core/introduction/ To install pymongo: easy_install pymongo PyMongo is the python interface with the mongo database. I have written a quick wrapper for it in data_processing/mongo.py You can test it works as: python ./data_processing/mongo.py
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