Utilizing Data Science techniques to answer questions

Machine learning is taking its space in almost every field. There are no questions that can’t be answered through data science. In this blog, we will understand the process of building a perfect product recommendation system using machine learning techniques. These systems are one of the most successful and widespread applications of machine learning in business.

Understanding the Product Recommendation System

Let’s talk about questions first. When we book a ticket on the IRCTC e-ticketing application, how come the advertisements we encounter there are related to our previous activities on our own devices? Ever wondered, why YouTube suggests to us only those videos that we are interested in watching? Why does Google always filter our search results based upon our likings? The answer to all these questions is product recommendation.

Still not ringing a bell? Let us explain more about such systems.

Technically speaking, a product recommendation system is a software tool designed to provide suggestions for content a specific user would like to purchase or engage with. Every single technical giant is using such systems to provide personalized experiences to its customers. For example:

• Amazon uses this system to suggest good books to its readers.

• Netflix has proven itself a niche player in providing video content to its users.

What are the basic types of connections a PRS creates?

• User-product relationships

• User-user relationships

• Product-product relationships

A basic principle of data science is to collect data and then use this data to answer questions. That’s exactly what a product recommendation system works upon.

What Machine Learning has to offer in building a PRS?

Several machine learning algorithms are used to create different types of product recommendation systems. Broadly, there are four categories of algorithms to accomplish this task.

Machine Learning Algorithms for Product Recommendation:

Content-based Filtering (CBF)

CBF creates a profile based on a user’s actions such as products bought or clicked on, web pages viewed, time spent browsing various product categories, etc. This information is then used to make recommendations.

Collaborative Filtering (CF)

CF works on the principle of similarity in preferences. The CF algorithm analyses information collected from users’ behaviours. For example, user A likes to listen to EDM, Bollywood, and acoustic music on an android music application like Spotify. User B likes to listen to EDM, Bollywood, and folk music on the same application. Spotify will then suggest acoustic music to user B and folk music to user A as the CF algorithm will determine that the two users have similar tastes.

Complementary Filtering

Suppose a user purchases a smartphone from Amazon’s online shopping store. On a revisit, this user most probably will go for accessories like headphones or a power bank. These algorithms determine the probability of multiple products being bought together. They are product-defined algorithms as they are based upon recommending products that are complementary to other products.

Hybrid Recommendation Systems

By the amalgamation of CBF and CF methods, hybrid recommendation systems can be built. These systems work upon content-based and collaborative-based approaches both.

Surely, a foolproof PRS can be built using ML Algorithms. But, why adopt it?

Benefits of implementing a Product Recommendation System:

• Boost in sales and revenues

• Positive effect on the user’s experience

• Increase in brand affinity

• Customer satisfaction

• Upsurge in click-through rates

The Challenging Part

Certain challenges need to be overcome in the deployment of a Product Recommendation System:

Cold Start

Cold start problems are further categorized into user cold start and product cold start. When a new user enters a website or an app, the system fails to recommend anything as no previous searches are available. This class of problems comes under user cold start. Similarly, when a new product is launched, there is no data available on the popularity of that product.

Scalability

A Product Recommendation Algorithm works well with small data-sets, but it starts producing inaccurate results with large ones.

Accuracy

If a Product Recommendation System can correctly predict the item preferences of each user then only it is considered as a reliable system.

Data Sparsity

Sometimes, we tend not to rate certain items. A sparse matrix is created as a result of insufficient data about such items.

Wrapping Up

The efficiency of a Product Recommendation System largely affects the success of a business as well as the satisfaction of the customers. As we have already seen, Machine Learning is the perfect tool to build a PRS. Go ahead and choose the best direction for your business.