Recommendation systems are used by E-commerce sites to suggest products to their customers.
Applications and sites using RS are :
Amazon, Google, Flipcart, Netflix, Linkedin etc.
2. Agenda
Introduction
Why RS are used ?
Goal and Objective
Methodology / Planning of work
Columns in Resultant DF
Product Recommendation for Germany
Tools and Technologies
Issues and Challenges
Benefits of RS
3. Introduction
Recommendation systems are used by E-commerce sites to suggest
products to their customers.
The product can be recommended based on an analysis of the past buying
behaviour of the customer as a prediction for future buying behaviour.
5. Goal and Objective
To build a recommender system for e-commerce websites with the help of
machine learning algorithms for customising the recommendations as
much as possible for the customers.
To help the customers to get personalized recommendations , help
customers to take correct decisions in their online transactions, and to
increase the sales.
10. Columns in Resultant Dataframe
Columns in the resultant DF are :
Antecedents, Consequents, Antecedent support, consequent support,
support, confidence, lift, conviction and leverage.
13. Issues and Challenges
Lack of Data
Cold start Problem
Synonymy
Shilling Attacks
Changing Data
14. Benefits of Product RS
Increase Revenues
Create customer satisfaction
Personalize individual interest
Reduce Time and effort
Boost number of items per order