Recommendation using ALS for implicit data. Code for Medium Blog

ALS_implementation.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jun 23 22:20:58 2019

@author: himansh
"""
#import libraries
import sys
import pandas as pd
import numpy as np
import scipy.sparse as sparse
from scipy.sparse.linalg import spsolve
import random



from sklearn.preprocessing import MinMaxScaler

import implicit 
from datetime import datetime, timedelta


#Data Preprocessing
def create_data(datapath,start_date,end_date):
    df=pd.read_csv(datapath)
    df=df.assign(date=pd.Series(datetime.fromtimestamp(a/1000).date() for a in df.timestamp))
    df=df.sort_values(by='date').reset_index(drop=True) # for some reasons RetailRocket did NOT sort data by date
    df=df[(df.date>=datetime.strptime(start_date,'%Y-%m-%d').date())&(df.date<=datetime.strptime(end_date,'%Y-%m-%d').date())]
    df=df[['visitorid','itemid','event']]
    return df


#Download the kaggle RetailRocket data and give the events.csv file path
datapath= 'events.csv'
data=create_data(datapath,'2015-5-3','2015-5-18')
data['visitorid'] = data['visitorid'].astype("category")
data['itemid'] = data['itemid'].astype("category")
data['visitor_id'] = data['visitorid'].cat.codes
data['item_id'] = data['itemid'].cat.codes

data['event']=data['event'].astype('category')
data['event']=data['event'].cat.codes



sparse_item_user = sparse.csr_matrix((data['event'].astype(float), (data['item_id'], data['visitor_id'])))
sparse_user_item = sparse.csr_matrix((data['event'].astype(float), (data['visitor_id'], data['item_id'])))

#Building the model
model = implicit.als.AlternatingLeastSquares(factors=20, regularization=0.1, iterations=20)


alpha_val = 40
data_conf = (sparse_item_user * alpha_val).astype('double')


model.fit(data_conf)

###USING THE MODEL

#Get Recommendations
user_id =   14
recommended = model.recommend(user_id, sparse_user_item)
print(recommended)




#Get similar items
item_id = 7
n_similar = 3
similar = model.similar_items(item_id, n_similar)
print(similar)