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

Popular posts from this blog

gRPC with Java : Build Fast & Scalable Modern API & Microservices using Protocol Buffers

gRPC Java Master Class : Build Fast & Scalable Modern API for your Microservice using gRPC Protocol Buffers gRPC is a revolutionary and modern way to define and write APIs for your microservices. The days of REST, JSON and Swagger are over! Now writing an API is easy, simple, fast and efficient. gRPC is created by Google and Square, is an official CNCF project (like Docker and Kubernetes) and is now used by the biggest tech companies such as Netflix, CoreOS, CockRoachDB, and so on! gRPC is very popular and has over 15,000 stars on GitHub (2 times what Kafka has!). I am convinced that gRPC is the FUTURE for writing API for microservices so I want to give you a chance to learn about it TODAY. Amongst the advantage of gRPC: 1) All your APIs and messages are simply defined using Protocol Buffers 2) All your server and client code for any programming language gets generated automatically for free! Saves you hours of programming 3) Data is compact and serialised 4) API ...

What is Big Data ?

What is Big Data ? It is now time to answer an important question – What is Big Data? Big data, as defined by Wikipedia, is this: “Big data is a broad term for  data sets  so large or complex that traditional  data processing  applications are inadequate. Challenges include  analysis , capture,  data curation , search,  sharing ,  storage , transfer ,  visualization ,  querying  and  information privacy . The term often refers simply to the use of  predictive analytics  or certain other advanced methods to extract value from data, and seldom to a particular size of data set.” In simple terms, Big Data is data that has the 3 characteristics that we mentioned in the last section – • It is big – typically in terabytes or even petabytes • It is varied – it could be a traditional database, it could be video data, log data, text data or even voice data • It keeps increasing as new data keeps flowing in This kin...

GraphQL - A Short Intro

Why GraphQL is the future of APIs Since the beginning of the web, developing APIs has been a difficult task for developers. The way we develop our APIs must evolve with time so that we can always build good, intuitive and well-designed APIs. In the last few years, GraphQL has been growing in popularity among developers. A lot of companies have started adopting this technology to build their APIs. GraphQL is a query language developed by Facebook in 2012 and released publicly in 2015. It has been gaining a lot of traction. It has been adopted by a lot of big companies such as Spotify, Facebook, GitHub, NYTimes, Netflix, Walmart, and so on. In this series of tutorials, we’re going to examine GraphQL, understand what it is, and see what features make this query language so intuitive and easy to use. So, let’s get started by examining the problems with REST, and how GraphQL solves them. We will also find out why companies have been building their APIs with GraphQL, and ...