Amazon Product Recommender System
Recommeder system creates a similarity between the user and items and exploits the similarity between user/item to make recommendations.
Created on 7th January 2023
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Amazon Product Recommender System
Recommeder system creates a similarity between the user and items and exploits the similarity between user/item to make recommendations.
The problem Amazon Product Recommender System solves
It can help the user to find the right product.
It can increase the user engagement. For example, there's 40% more click on the google news due to recommendation.
It helps the item providers to deliver the items to the right user.In Amazon , 35 % products get sold due to recommendation.
It helps to make the contents more personalized.In Netflix most of the rented movies are from recommendations.
Challenges we ran into
There are mainly 6 types of the recommendations systems :-
Popularity based systems :- It works by recommeding items viewed and purchased by most people and are rated high.It is not a personalized recommendation.
Classification model based:- It works by understanding the features of the user and applying the classification algorithm to decide whether the user is interested or not in the prodcut.
Content based recommedations:- It is based on the information on the contents of the item rather than on the user opinions.The main idea is if the user likes an item then he or she will like the "other" similar item.
Collaberative Filtering:- It is based on assumption that people like things similar to other things they like, and things that are liked by other people with similar taste. it is mainly of two types: a) User-User b) Item -Item
Hybrid Approaches:- This system approach is to combine collaborative filtering, content-based filtering, and other approaches .
Association rule mining :- Association rules capture the relationships between items based on their patterns of co-occurrence across transactions.