Recommendation Engine part-1

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Recommendation Engine Introduction 


        Today we are going to start our exploration of data mining by looking at recommendation engine. People call this mixed words as a single effective word with different names like Recommendation engine, Recommendation system.

What we will learn:

To begin the tour of recommendation engine , I ‘am going to answer four basic question about Recommendation Engine.

  1. What is Recommendation Engine ?

  2. What is the difference between Real life Recommendation engine and online Recommendation Engine ?

  3. Why should we use recommendation engines?
  4. What are the different types of Recommendation Engines ?

What is Recommendation Engine ?

Wiki Definition :

Recommendation Engines are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that user would give to an item.

dataaspirant Definition:

Recommendation Engine is a black box which analysis some set of users and shows the items which a single user may like.

What is the difference between Real life Recommendation engine and online Recommendation Engine ?

Before summarizing the difference between Real life Recommendation engine and online Recommendation Engine lets see them individually.

Real life Recommendation Engine:

Your friend as movie recommendation engine:


Most of the time we will ask our friends to recommend some good movies to see and most of the cases we will feel the movies which recommended by our friends is good ones.

Your sister/mother/father/brother/friend as dress Recommendation Engine:


       Selecting an dress from thousands of models is little bit harder that’s why, when we are going to buy a good dress for our Birthday or for any festival purpose we ask our sister/mother/father/brother/friend to select a good dress for us.

Your instructor as Book Recommendation Engine:


     When we want to read one good book for better understand of particular concept we will ask our instructor  to recommend some good books for better understanding of concept.

Your elder brother or elder sister as career recommendation engine:


We always consider our elder sister or brother suggestions in our career planing.


In all these cases the person who is recommended things for you is well know about you and about the things you want to recommend.

Online Recommendation engine:

Facebook People You May Know:


People You May Know are people on Facebook that you might know. it shows you people based on mutual friends, work and education information, networks you’re part of,contacts you’ve imported and many other factors.

Netflix Other Movies You Might Enjoy:


Netflix offers thousands of titles to stream ,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 movies 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 You may be interested in


The Jobs You May Be Interested In feature shows jobs posted on LinkedIn that match your profile in some way. These recommendations shown based on the titles and descriptions in your previous experience. If you search for jobs in your field and “save your search”. You’ll receive alerts whenever a new job is posted within your search rules. That might help you find jobs you’re looking for without altering your profile information.


Amazon Customers who Brought this Item Also Bought


Amazon uses it’s Recommendation engine to Recommend products to customers to bought. This customers who bought this Item Also Bought played a key role to increase Amazon sales.

Let’s summarize these two recommendation engines. In real life recommendation engine the main theme is 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. don’t feel any confuse about similar persons and all these stuff in very next post i will explain these things in much clear way.

Why should we use recommendation engines?

There is one famous quote about customers relationship  summary of quote will go like this customers don’t know what they want until we show them if we succeed in showing some thing which customers  may like business profit will sky rocket .

so recommendation engines will help customers find information , products  and services they might not have thought of. Recommendation application can be found in wide variety of industries and Business. some of them we have seen before and some application listed below.

  • Travel
  • Financial service
  • Music/Online radio
  • Tv and Videos
  • Online publications
  • Retail
  • and countless others….

 What are the different types of Recommendation Engines ?

Recommendation engines are mainly 2 types and one hybrid type:

1) Collaborative filtering

2) Content-based filtering

3) Hybrid Recommendation Systems

Collaborative Filtering:

userbasedpeople with similar taste to you like the thing you like.

Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like 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 and therefore it is capable of accurately recommending complex items such as movies without requiring an “understanding” of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the  k-nearest neighbor (k-NN) approach and the Pearson Correlation.

Content-based filtering


people who liked this also liked these as well

Content-based filtering methods are 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; beside, 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 that 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 be implemented in several ways, by making content-based and collaborative-based predictions separately and then combining them, by adding content-based capabilities to a collaborative-based approach (and vice versa), or 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).

Upcoming posts:

In very next upcoming posts we are going to learn about these 3 Recommendation system in big picture and we are going to learn how to implementation them.

Follow us:


I hope you liked todays post. If you have any questions then feel free to comment below.  If you want me to write on one specific topic then do tell it to me in the comments below.

Thank’s for Reading…. 

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14 Responses to “Recommendation Engine part-1

  • Jithin Joe
    2 years ago

    Hi Blogger;
    First up all lemme congrats you for ur work, U r doing a wonderful Job by providing the basic ideas abt Recommendation Engine. and also I hope u can give me more insight abt the same, I am doing a project work on the same for my final year Engg. I Hope to hear from u soon.

    • Thank’s jithin joe soon i will post all stuff related to Recommendations. All the best for your final year project.

  • Anonymous
    2 years ago

    Very Nice article !!!
    – Janardhan Shetty

  • Simply fantastic article ez to understand the basics how we are recommended… gr8 work keep doing

  • Anonymous
    1 year ago

    Hi SaiMadhu
    I must say the way you have composed this article is simply AWESOME !!!
    I work in the DS field and there are very few authors / bloggers/ writers who could express their views in such a lucid and interesting manner. FULL MARKS TO YOU. Very Good Job!!
    I am eagerly waiting for part 2 of the Recommendation Engine. Please make it soon…
    Pravin Bhosale

    • Thanks Pravin Bhosale. i have posted similarity measures as continues to recommendation engine post. just have a look on that.

  • Anonymous
    1 year ago

    gain newknowledge


  • hello was hoping you would have posted the following parts about the recommendation system as promised though the introduction was a great read thanx

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