Introduction to Recommendation engine
Introduction to Recommendation Engine
Today we are going to start our exploration of machine learning by looking at recommendation engine. People call this mixed words as a single effective word with different names like the Recommendation engine, Recommendation system.
What we will learn:
To begin the tour of the recommendation engine, we are going to answer four basic question about Recommendation Engine.
What is Recommendation Engine?
What is the difference between Real life Recommendation engine and online Recommendation Engine?
- Why should we use recommendation engines?
What are the different types of Recommendation Engines?
What is Recommendation Engine?
Recommendation Engines are a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ that user would give to an item.
Recommendation Engine is a black box which analysis some set of users and recommends the items which a user may like. The analysis uses the user details like.
- Geographical location,
- Online searches,
- Previous purchase or items user interested.
Before we learn deeper aspects of recommendation engines. Let’s first understand about the real life and online recommendation engines.
What is the difference between Real life and online Recommendation Engine
Before summarizing the difference between Real life Recommendation engine and online Recommendation Engine lets qucikly look at the individual examples.
Real life Recommendation Engine:
- Your friend as movie recommendation engine.
- We ask our friends recommend some good movies for the weekend. Most of the cases we enjoy the movies recommended by our friend.
- Your family members or friends as dress Recommendation Engine.
- Selecting a dress from thousands of models is a little bit harder. That’s why, when we are going to buy a dress for our Birthday or any festival purpose we ask our family members or friends to select a good dress for us.
- Our course instructor as a book Recommendation Engine.
- When we want to read one good book for better understand of particular concept we will ask our instructor to recommend a good book.
In all the above cases the person who is recommended things for you. Is well known about you and about the things you like. So it’s so easy for them to recommend things to you.
Online Recommendation engine:
Facebook People You May Know:
People You May Know on Facebook that you might know. It shows you the people based on mutual friends, work and education information, networks you’re part of, contacts you’ve imported and many other factors.
Movies You Might Enjoy from Netflix:
Netflix offers thousands of movies stream to you, when you fill out your Taste Preferences or rate movies and TV shows, you’re helping Netflix to filter through the thousands of selections to get a better idea of what you would like to watch. Netflix recommendation algorithm takes certain factors into consideration to recommend the movie to you, such as:
- The genres of movies and TV shows available.
- Your streaming history, and previous ratings you’ve made.
- The combined ratings of all Netflix members who have similar tastes in titles to you.
Linkedin Jobs, ou may be interested in
The Jobs You May Be Interested In feature shows jobs posted on LinkedIn that match your profile. These recommendations are shown based on the titles and descriptions on your previous experience. If you search for jobs in your field and “save your search.”
You’ll receive alerts whenever a new job posted within your search rules. That might help you find the job you’re looking for without altering your profile information.
Amazon Customers who Bought this Item Also Bought
Amazon uses it’s Recommendation engine to Recommend products to customers to bought. The customers who bought this Item also bought played a vital role to increase Amazon sales.
Let’s summarize these two recommendation engines.
- In real life recommendation engine:
- I will like the thing which you may believe I will like.
- In online recommendation engine:
- I may like the things which you may like if you and me are more similar persons.
Why should we use recommendation engines?
There is one famous quote about customers relationship. The Summary of the quote will go like this customers don’t know what they want until we show them. If we succeed in showing something which customers may like business profit will skyrocket .
So recommendation engines will help customers find information, products, and services they might not have thought of. Recommendation applications are helpful in a wide variety of industries and Business.
Some of them we have seen before and some application listed below.
- Financial service
- Music/Online radio
- Tv and Videos
- Online publications
- and countless others….
Different types of Recommendation Engines
Recommendation engines are mainly 2 types and one hybrid type:
- Collaborative filtering.
- Content-based filtering
- Hybrid Recommendation Systems
Collaborative filtering methods based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences. Using the user information to predict what users will like. The predictions are based on their similarity to other users.
A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content. Therefore it is capable of accurately recommending complex items such as movies without requiring an “understanding” of the item itself.
You can learn how to implement the collaborative filtering recommendation engine in Python
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Content-based filtering method based on a description of the item and a profile of the user’s preference. In a content-based recommendation system, keywords are used to describe the items. A user profile is built to indicate the type of item this user likes.
In other words, these algorithms try to recommend items that are similar to those a user liked in the past (or is examining in the present). In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended. This approach has its roots in information retrieval and information filtering research.
Hybrid Recommendation Systems
Recent research has demonstrated that a hybrid approach, combining collaborative filtering and content-based filtering could be more effective in some cases. Hybrid approaches can implement in several ways.
Creating content-based and collaborative-based predictions separately and then combining them, by adding content-based capabilities to a collaborative-based approach (and vice versa) By unifying the approaches into one model.
Several studies empirically compare the performance of the hybrid with the pure collaborative and content-based methods and demonstrate that the hybrid methods can provide more accurate recommendations than pure approaches. These methods can also be used to overcome some of the common problems in recommendation systems such as cold start and the sparsity problem.
Netflix is a good example of hybrid systems. They make recommendations by comparing the watching and searching habits of similar users (i.e. collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering).
I hope you like this post. If you have any questions, then feel free to comment below. If you want me to write on one particular topic, then do tell it to me in the comments below.