Are you curious to know “What is a Recommendation Engine?”
Let us go a little back in the historical timeline to see how people used to do shopping.
Back in the days when there was no internet, the choices in shopping for food, clothes, shoes, books, and electronics were limited. Basically, everything was done following the suggestions given by friends or commercials done by famous personalities.
In the post-internet era, when internet use was limited, the best shopping suggestions were programmed by coders prior to the application itself. Thus, the suggested options were few and the exact same for every buyer.
Today, in the era of Artificial Intelligence/Machine Learning and cloud-based software applications like Instagram, Amazon, Google, and many more, the best-sellers are recommended to consumers in real-time as per the big data based on their search history, purchasing habits, and other patterns.
This very task of customised suggestions of the products in real-time, at a large scale, is done by a Recommendation Engine.
Let us read more into this blog and understand it a bit more to learn its functions better.
- Understand the meaning of a Recommendation Engine
- Learn what a Recommendation Engine does
- Know the examples of a Recommendation Engine
What is a Recommendation Engine?
Spotify suggests Trending Songs, Bests of the Artists, and other recommendations based on your music listening history.
On the other hand, if you search for clothes in an app like Myntra, you will see the same product in every Google ad, in Instagram-sponsored products, and on every website, you open. The principle system working behind this feature is the Recommendation Engine.
The Recommendation System is a data filtering tool that uses AI/ML-based algorithms to provide the most personalised and relevant choices to the user for products, services, and other information, enhancing the user experience.
The recommendations are generated by finding behaviours and patterns in the big data gathered by the cloud-computing engine from the user’s search history, bank transactions, and other credentials.
How does a Recommendation Engine work?
The Recommendation engine’s functions are based on AI-built algorithms, deep learning solutions, and predictive data analytics processes.
AI algorithms select the apt filters of the recommender system based on situational analysis.
The deep learning recommendation system undergoes the following 4 steps basically:
- Collecting Data
- Storing Data
- Data Analysis
- Filtering the Data
1. Collecting Data
Implicit Data, taken from their online search history, order transaction details, demographics, cart logs, or clicks, and Explicit Data given explicitly by a user, like ratings, likes, or reviews, are collected.
2. Storing Data
At this stage, the collected big data of millions of users is stored and assessed in suitable cloud databanks segregated as per the data type.
3. Data Analysis
Here, the segregated data undergoes deep analytics periodically on a real-time basis.
4. Filtering the Data
In this final stage, the fine data passes through various rules, algorithms, and formulae based on the respective recommendation system.
Benefits of Recommendation Systems
- User Satisfaction
- User Interaction
- Sales Conversion
- Novelty to Users
- Consumer Confidence
1. User Satisfaction
Gives increased user satisfaction to the customer, as well as desired results of customer retention for the businesses.
2. User Interaction
Increases the time a reader spends online, thus increasing customer and brand interaction by showing searches as per their preferences.
3. Sales Conversion
Increased sales conversion without increasing marketing investment.
4. Novelty to Users
It offers novel ideas and suggestions to users that were not known to them prior.
5. Consumer Confidence
Increased customer confidence in the brand and thus increasing the chances of future purchases.
Recommendation Engine Examples
Let us now see the major types of Recommendation Engines and some of their examples:
1. Collaborative Filtering System
It detects patterns of user-product interaction in order to give suitable recommendations that similar users like.
We can find this filter system on platforms like Netflix, Spotify, Twitch, Amazon, etc.
Collaborative Filtering consists of the following 2 types of recommendation systems:
- Item-Item Filtering
- User-User Collaborative Filtering
It analyses individual product ratings given by a customer, for example, their likes, dislikes, ratings, reviews, comments, etc. and then, based on past ratings, gives further product recommendations to that customer with similar views and ratings. The more users on the platform, the better the recommender system for a user.
For Example, Goodreads’ “Readers Also Enjoyed” and Amazon’s “Customers Who Viewed This Item Also Searched”.
User-User Collaborative Filtering
It is built upon filtering the results as per the similar tastes of multiple users.
For Example, Youtube’s “Recommended Channels” as per history.
2. Content-based Filtering System
Here, similar suggestions are given as per the genres, attributes, or specifications of the desired product.
For Example, on Amazon, if you find Badminton Rackets, you get suggestions with “Similar Results”, “Bestsellers in Rackets”, “Items bought With This Product”, “People also Frequently Bought”, and many more.
3. Hybrid Recommendation Engine
It fills the gaps of both the above recommender engines, offering a more accurate and broader product range to consumers. It finds the suggestions based on the particular user’s likes and dislikes, the specific and similar attributes of the product itself, and recommendations as per similar users’ choices as well.
For Example, Netflix’s “Trending in India”, “Surprise Me”, “Because You Watched’, More in Thrillers and so on.
Deep Learning based and AI-Recommendation Engines are very powerful tools as a marketing strategy for targeting the challenges to satisfy the audience’s demands and for customer retention and engagement. While enhancing the user experience, it also generates more business revenue and targets many. That makes it crucial to design an effective recommender system for a successful business.
Frequently Asked Questions (FAQs)
1. What does a recommendation engine do?
Today in the era of AI/ML and cloud-based software applications like Instagram, Amazon, Google, and many more, the best-sellers are recommended to consumers in real-time as per the big data of their search history, purchasing habits, and other patterns. This task of a customised recommendation of the products in real-time and on a large scale is done by a Recommendation Engine.
2. What is an example of a recommendation engine?
If you search for clothes in an app like Myntra or Flipkart, you will see the same product in every Google ad, in Instagram-sponsored products, and on every website you open. The principle system working behind this feature is the Recommendation Engine.
3. What do you mean by a recommendation engine?
The Recommendation System is a data filtering tool that uses AI/ML-based algorithms to provide the most personalised and relevant choices to the user for products, services and other information, enhancing the user experience.
4. What is a recommendation engine in machine learning?
Neural Networks and Deep Learning Methods make the future patterns, algorithms, and rules for machine learning. The suggestions are generated by finding behaviours and patterns in the big data gathered by the cloud-computing engine from the user’s search history, bank transactions, and other credentials.