ML Based Hybrid Recommendation System: Driving Growth Of Today’s Businesses

How does YouTube, Amazon, Netflix, and many other apps keep recommending videos, products, or shows you may be interested in? Google follows your search topic everywhere a few minutes after you searched. Everyone’s feed is full of personalized topics, news, and accurate recommendations based on previous activities.

What Do They Use?

They use the recommender system to make recommendations about products, information, services, or audio-visual content. The majority of these systems use a hybrid approach to enhance the effectiveness and accuracy of recommendation.

Interesting? Isn’t it?

Let’s explore what hybrid recommendation system is, why it is important in the data-driven world and everything around it.

What Is A Recommender System?

Recommender System is one of the popular applications of machine learning. It predicts future preferences and suggests a set of relevant items to the users. In a general sense, recommender systems are software that generates recommendations that fulfill large information gaps and help users to access the items that can meet their preferences and needs.

What Is Hybrid Recommender System?

A hybrid recommendation system combines more than one method, model, or strategy in different ways to achieve better outcomes.

There is a wide number of approaches, algorithms, and methods that are used to develop RS. In today’s AI-driven environment, there is plenty of ML (Machine Learning) algorithms used in diverse and advanced applications such as self-driving vehicles, recommendation system, pattern recognition, virtual assistants, fraud detection, image recognition, and many others.

In this blog, we will talk about the hybrid recommendation system developed by SPEC INDIA’s team. It uses an unsupervised clustering algorithm to suggest the best possible business rule from the set of rules.

A Hybrid Recommendation System Powered By Machine Learning: Level Up Customer Engagement

You can find recommendation systems almost everywhere. A machine learning algorithm offers the most relevant suggestions almost about everything including food, music, news, post, and even people or page on social media networks.

Generally, machine learning algorithms for recommendation system categorized into two different approaches:

• Content-based filtering: based on similarities of items and similar features
• Collaborative filtering: based on user behavior and preferences for items

There is one more approach widely used in the recommendation system is – association rules. Association rule is an unsupervised machine learning method to discover the relationship between objects in large datasets. Unsupervised machine learning is a type of machine learning that deals with datasets having no pre-existing labels.

Associations rules mining is used to identify insights between different objects and frequent patterns in a database. It is widely used in Market Basket Analysis (find an association between items in retail), customer clustering, cross-selling, and in finding customer purchase patterns.

How Association Rule Works?

There are two main algorithms to perform association rules mining – FP Growth And Apriori. These algorithms are used to identify the strength of association between a pair of items purchased together.

What Is the Apriori Algorithm?

Apriori is an algorithm for frequent itemset mining and association rule learning in a dataset. It is the oldest algorithm popularly used for building association rules.

What Is FP Growth?

FP Growth is an improvement of the Apriori algorithm. Both these algorithms are used for frequent itemset mining. They take similar input and generate similar output.

What Is The Difference Between FP Growth And Apriori?

Apriori is an array-based algorithm whereas FP Growth is a tree-based algorithm.

Apriori uses breadth-first search and FP Growth uses a depth-first search.

One of the key differences between these algorithms is how they generate output. Apriori uses a level-wise approach, on the other side, FP Growth uses a pattern-growth approach.

FP Growth is faster than the Apriori algorithm. Apriori occupies larger memory space due to candidate generation and FP Growth requires less memory space as there is no candidate generation and compact design.

Why Recommendation System Is Important In Today’s Data-Driven World?

Data is an inseparable and integral part of today’s business regardless of the industry type and size. Users have a wide range of options to select from and that gives a huge opportunity to businesses to help customers meet their needs.

There is too much information and alternatives are available for everything and that’s the reason consumers are facing problems to choose from a wide range of collections. To help users select from a plethora of information, today’s businesses have implemented a recommendation system to narrow down choices and ultimately select best as per their needs.

From movies to restaurants and from books to hotel bookings, a recommendation system is designed to offer personalized recommendations based on user interests and previous activities along with insights.

Google, Netflix, Amazon, YouTube, Spotify, and almost all retailers have their recommendation systems to help users cope up with a selection. It is considered as an indispensable part of retail to drive growth.

The recommendation system is one of the popular, widely-implemented, and impactful applications of Machine Learning with a diverse set of algorithms and techniques.

SPEC INDIA Builds A Hybrid Recommendation System To Automate Decision-Making And Improve Performance

By using advanced machine learning solutions and custom Python code, SPEC INDIA’s team of data engineers have built a hybrid recommendation system for one of our leading clients.

This intelligent suggestion system is built with an aim to suggest business rules for the appropriate column by using historical data. This solution is designed to resolve the biggest problem of manually choosing business rules from the master business-rule data file.

It’s Time To Invest In Data To Drive Growth And Increase Revenue: Have You Started Utilizing It?

Every industry and every business aims to become data-driven.

If you are not managing it, storing it, and utilizing it effectively, you may be losing out on big opportunities. It’s a high time you consider investing in Business Intelligence and Analytics Services to increase user base and maximize growth rate.

It is better and rewarding to have a basic recommender system for your users to offer personalized suggestions and help them meet their needs effectively and quickly.

Thinking to try it? Let us know!