Skip to main content

Recommendation Engines - Know How

Recommendation Engines perform a variety of tasks - but the most important one is to find products that are most relevant to the user. Content based filtering, collaborative filtering and Association rules are common approaches to do so.

 So let's first Understand basics of Recommendation Engines and then we'll later on Build Our Own Recommendation Engine !!!

 HIGH QUALITY, PERSONALIZED ARE THE HOLY GRAIL FOR EVERY ONLINE STORE.
UNLIKE OFFLINE STORES, ONLINE STORES HAVE NO SALES PEOPLE.
USERS ON THE OTHER HAND HAVE LIMITED TIME AND PATIENCE, ARE NOT SURE WHAT THEY ARE LOOKING FOR ONLINE STORES HAVE A HUGE NUMBER OF PRODUCTS.
RECOMMENDATIONS HELP USERS NAVIGATE THE MAZE OF ONLINE STORES FIND WHAT THEY ARE LOOKING FOR FIND THINGS THEY MIGHT LIKE, BUT DIDN’T KNOW OF.
RECOMMENDATIONS HELP ONLINE STORES SOLVE THE PROBLEM OF DISCOVERY.
BUT HOW? Lets Explain this.

 ONLINE STORES HAVE DATA
1) WHAT USERS BOUGHT
2) WHAT USERS BROWSED
3) WHAT USERS CLICKED
4) WHAT USERS RATED

 All these DATA IS FED into Recommendation Engine to derive below:
TOP PICKS FOR YOU!!
IF YOU LIKE THIS, YOU’LL LOVE THAT!
IF YOU BUY THIS, YOU’LL NEED THAT!

Tasks Performed by Recommendation Engine

 Filter Relevant Products

 Sub Tasks: 1.1) PREDICT WHAT RATING THE USER WOULD GIVE A PRODUCT
1.2) PREDICT WHETHER A USER WOULD BUY A PRODUCT
1.3) RANK PRODUCTS BASED ON THEIR RELEVANCE TO THE USER

 HOW TO FIND RELEVANT PRODUCTS
1) “SIMILAR” TO THE ONES THE USER “LIKED” (Content Based Filtering)
2) “LIKED”BY “SIMILAR” USERS (Collaborative Filtering)
3) PURCHASED ALONG WITH THE ONES THE USER “LIKED” (Association Rules)

 RECOMMENDATION ENGINES NORMALLY USE ONE OR MORE OF THESE TECHNIQUES.

 BEFORE WE GO AHEAD, LET’S JUST CLARIFY -
HOW DO WE KNOW THAT A USER “LIKES” A PRODUCT?
Ans) THESE ARE PRODUCTS THE USER
              PURCHASED
              CLICKED ON
              ADDED TO CART
              RATED HIGHLY
(IF THE USER RATES SOMETHING LOW, THAT’S IMPORTANT DATA TOO!)
SOMETIMES, STORES ALSO ASK FOR EXPLICIT INPUT FROM USERS.

Comments

Post a Comment

Popular posts from this blog

Data Engineering - Tools & Intro

Data Engineering - Tools & Intro So I just realized that I am here after a month or so. I was busy at work and traveling. I am starting a kind of new series, I say it Data Engineering Series in which I will be discussing different tools. Of course, I am not able to discuss the entire concept of Data Engineering neither I know it as I will be learning myself. What is Data Engineering? Data Engineering is all about developing, maintaining systems that are responsible for transferring data in large volumes and make it available for analysts and data scientists to use it for analyzing and data modeling. Data engineering is a superset of Data Science or the subset, not clear to me but the collaboration of data engineers and scientists fruits useful data-driven solutions. Data Engineering tools It consists of several tools. Some are dealing with data storage while others with analysis and ETL. Ofcourse, Apache Kafka is one of them. The others tools that I might be covering are Apache...
Post a Comment
Read more

Creating REST API With Spring Boot

Let's Create a REST API With Spring Boot In this post, we will learn how to create REST API with Spring Boot, JPA, Hibernate, and MySQL. Create the Spring Boot Project. Define Database configurations. Create an Entity Class. Create JPA Data Repository layer. Create Rest Controllers and map API requests. Create Unit Testing for API requests and run the unit testing. Build and run the Project. Requirement We need to create a simple REST API to store user data to MySQL database with that API so we can create, update, delete, and get users information. Create the Spring Boot Project First, go to  Spring Initializr  and create a project with below settings Web  — Full-stack web development with Tomcat and Spring MVC DevTools  — Spring Boot Development Tools JPA  — Java Persistence API including spring-data-JPA, spring-orm, and Hibernate MySQL  — MySQL JDBC driver Actuator  — Production-ready features to help you monitor...
Post a Comment
Read more

Machine Learning & Deep Learning

Machine Learning & Deep Learning Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two concepts you’ve likely heard of before:  machine learning  and  deep learning . These terms are often thrown around in ways that can make them seem like interchangeable buzzwords, hence why it’s important to understand the differences. And those differences should be known! Examples of machine learning and deep learning are everywhere. It’s how Netflix knows which show you’ll want to watch next or how Facebook knows whose face is in a photo. And it’s how a customer service representative will know if you’ll be satisfied with their support before you even take a customer satisfaction (CSAT) survey. So what are these concepts that dominate the conversations about artificial intelligence and how exactly are they different? What is machine learning? Here’s a basic definition of machine learn...
Post a Comment
Read more