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Build a recommendation engine in 10 minutes using Recombee in Python

We’ll build a recommendation engine based on Coursera dataset to recommend courses to user by using a third party service called recombee.

Photo by Morning Brew on Unsplash

What is a recommendation engine?

Source: Human for AI

How does recombee works?

  • Detail Views (User has viewed details of an item)
  • Cart Addition (User has added an item in the cart)
  • Rating (User has rated an item)
  • Bookmarks (User has saved/bookmarked an item)
  • Purchases (User has purchased an item)

Getting Started with Recombee

$ pip install recombee-api-client
from recombee_api_client.api_client import RecombeeClient
from recombee_api_client.api_requests import *
import random
import pandas as pd


course_df = pd.read_csv("coursea_data.csv", index_col=0)
user_df = pd.read_excel (r'user.xlsx')
client.send(AddItemProperty('course_title', 'string'))
client.send(AddItemProperty('course_organization', 'string'))
client.send(AddItemProperty('course_certificate_type', 'string'))
client.send(AddItemProperty('course_rating', 'double'))
client.send(AddItemProperty('course_difficulty', 'string'))
client.send(AddItemProperty('course_students_enrolled', 'string'))
client.send(AddUserProperty('citizenship', 'string'))
client.send(AddUserProperty('email', 'string'))
client.send(AddUserProperty('full_name', 'string'))
client.send(AddUserProperty('gender', 'string'))
requests = [SetItemValues(
course_df.index[i], #itemId
"course_title": course_df['course_title'][i],
"course_organization": course_df['course_organization'][i],
"course_certificate_type": course_df['course_Certificate_type'][i],
"course_rating": course_df['course_rating'][i],
"course_difficulty": course_df['course_difficulty'][i],
"course_students_enrolled": course_df['course_students_enrolled'][i]
cascade_create=True # Use cascadeCreate for creating item
# with given itemId if it doesn't exist
) for i in range(len(course_df))]
# Send catalog to the recommender system
user_requests = [SetUserValues(
row['id'], #itemId
"citizenship": row['citizenship'],
"email": row['email'],
"full_name": row['first_name'],
"gender": row['gender']
cascade_create=True # Use cascadeCreate for creating item
# with given itemId if it doesn't exist
) for idx, row in user_df.iterrows()]
# Send catalog to the recommender system
#user11 has viewed item0, item104, item107 and rated item110.client.send(AddDetailView('11','0', cascade_create=True))
client.send(AddDetailView('11','104', cascade_create=True))
client.send(AddDetailView('11','107', cascade_create=True))
client.send(AddRating('11','110', 0.5, cascade_create=True))# Rating rescaled to interval [-1.0,1.0],
# where -1.0 means the worst rating possible, 0.0 means neutral, and 1.0 means absolutely positive rating.
# For example, in the case of 5-star evaluations, rating = (numStars-3)/2 formula may be used for the conversion.
# So here user rated 4/5 to a course (4-3)/2 = 0.5
#Also the rating here will have no effect on the rating of item110 that is available in the dataset.
#user13 has viewed item109, item110, item0, item103, item101, item104.
client.send(AddDetailView('13','109', cascade_create=True))
client.send(AddDetailView('13','110', cascade_create=True))
client.send(AddDetailView('13','0', cascade_create=True))
client.send(AddDetailView('13','103', cascade_create=True))
client.send(AddDetailView('13','101', cascade_create=True))
client.send(AddDetailView('13','104', cascade_create=True))
#user14 has viewed item10, item11 and purchased item115.
client.send(AddDetailView('14','10', cascade_create=True))
client.send(AddDetailView('14','111', cascade_create=True))
client.send(AddPurchase('14','115', cascade_create=True))
#user15 has viewed item113, item111, item 105, item102.
client.send(AddDetailView('15','113', cascade_create=True))
client.send(AddDetailView('15','111', cascade_create=True))
client.send(AddDetailView('15','105', cascade_create=True))
client.send(AddDetailView('15','102', cascade_create=True))
#user17 has viewed item140, item142, item15, item151, item144 and purchased item15.
client.send(AddDetailView('17','140', cascade_create=True))
client.send(AddDetailView('17','142', cascade_create=True))
client.send(AddDetailView('17','15', cascade_create=True))
client.send(AddDetailView('17','151', cascade_create=True))
client.send(AddDetailView('17','144', cascade_create=True))
client.send(AddPurchase('17','15', cascade_create=True))
recommended = client.send(RecommendItemsToUser('11',5))
{'recommId': 'e30ae1d99a1dae19155a6643f1214d0c', 
'recomms': [
{'id': '200'},
{'id': '807'},
{'id': '565'},
{'id': '871'},
{'id': '664'}
  • The user only viewed beginner or intermediate level courses and recommendations are all within that.
  • Rating of recommendation is between 4.6–4.8 based on the four interactions.
  • Certificate type was also only course and specialization and we got recommendations based on that.
  • The courses user viewed is in general 2 categories i.e. Python and Design the recommendations we got are in those categories.




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