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.
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.
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.
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.
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.
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.
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.
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.
Boost in sales and revenues
Positive effect on the user’s experience
Increase in brand affinity
Customer satisfaction
Upsurge in click-through rates
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.
A Product Recommendation Algorithm works well with small data-sets, but it starts producing inaccurate results with large ones.
If a Product Recommendation System can correctly predict the item preferences of each user then only it is considered as a reliable system.
Sometimes, we tend not to rate certain items. A sparse matrix is createdAtAt as a result of insufficient data about such items.
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.
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