How Recommender Systems Are Changing E-Commerce

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Recommender systems suggest products and content to users, helping them navigate through endless virtual marketplaces. While traditional approaches have proven effective, artificial intelligence and machine learning have the potential to take these systems to the next level.

Until very recently, skilled salespeople needed to be present in physical stores to recommend products to customers. But not anymore.

These days, recommender systems act as virtual salespeople — helping online shoppers navigate through products, recommending items based on search history and demonstrated interests and, in general, making their online shopping experience much more enjoyable.

This article will explore the various aspects of recommender systems and how they’re changing commerce as we know it. (Also read: How Artificial Intelligence Will Revolutionize the Sales Industry.)

What is a Recommendation System?

A recommender system, or ‘recommendation system,” is an engine that recommends, content and/or products to consumers based on past behavior and other metrics.

The term “recommender system” (RS) is relatively new in common vernacular, but the basic concept of recommendation has been around for a long time: Think about how you made consumption decisions before the internet. Most likely, you relied largely on peer opinions to decide what to buy, what to wear and/or what to watch. In a sense, these peer opinions are like manual recommendations.

In the world of computing, recommendation systems were first introduced in 1979. Primitive iterations included a computer-based librarian called Grundy who would suggest suitable books to readers. After this basic recommender system, the first commercial RS, called Tapestry, was introduced in 1990. Another similar system, GroupLens, was introduced around the same time. But the RS “revolution” didn’t kickstart until the late 1990s, when Amazon introduced Collaborative Filtering: the most popular recommendation technology to this day.


Today, recommender systems are emerging continuously and are a very popular research area. Their growth is largely due to the growth of the internet and big data, and they are mainly impacting e-commerce and online shopping. (Also read: 4 New Technologies Making Waves in the E-commerce Sector.)

How do Recommender Systems Work?

The core of recommendation system is based on recommendation approaches.

The most common approaches followed in recommendations are:

Collaborative Filtering

Collaborative filtering is based on the concept of people-to-people co-relation. Put simply, that means two or more individuals sharing common interests in one area are likely to be attracted to similar items or products in other areas too.

The similarity between individuals can be tracked by studying things like their browsing patterns, search options, purchase history and ratings.

Collaborative filtering is the most common approach recommendation systems follow.

Content-Based Filtering

Content-based filtering focuses on consumers individually.

This type of system recommends similar products and content to a user based on the products and content they’ve consumed or liked in the past. The assumption behind this system is, if a user likes an item “A” from a category “X,” they may also like item “B” from category “X” or item “A” from category “Y.”

The negative side of this system is that it always shows the same types of items, which can make the shopping experience monotonous and boring.

Knowledge-Based Filtering

In knowledge-based filtering, recommendations are made based on the system’s domain knowledge. In other words, a knowledge-based filtering system captures user requirements, pairs them with a specific knowledge base and makes recommendations based on that.

Demographic Filtering

This types of system recommends based on the user’s demographic data.

Demographic filtering is less personalized than other filtering approaches, but it can be useful for making recommendations to new users who may not have a browsing/purchase history on a particular platform.

Community-Based Filtering

Community-based recommender systems are driven by the user’s peers’ browsing and purchase history, rather than their own. It is based on the concept that a user is more likely to be influenced by their friends’ recommendations rather than random suggestions.

Hybrid Filtering Systems

Hybrid filtering combines multiple filtering approaches to recommend the most appropriate products/content.

The benefit of this system is to maximize the benefits of each filtering system while downplaying their shortcomings.

Popular Recommendation Systems

Recommendation systems are present in almost all platforms online — from streaming services, to social media, e-commerce and app stores.

Some notable services that rely on recommender systems include:

  • Netflix. Over-the-top (OTT) and video-on-demand (VOD) platforms like Netflix depend on recommendation systems to help users get their favourite movies and series. (Also read: The Role of Knowledge Graphs in Artificial Intelligence.)
  • Spotify. Spotify uses recommendation engines to recommend users audio content.
  • Amazon. The market leader in the e-commerce space, Amazon is based on various artificial intelligence (AI) and machine learning (ML) recommendation engines. Amazon is a trailblazer in this sector of technology.
  • Facebook. Facebook uses recommendation systems to suggest friends and advertisements to users.
  • Google. While Google uses recommender systems in various areas, its Google Play Store, in particular, makes optimized and efficient app suggestions.

How to Make A Recommendation System

There are many types of recommendation systems, most of which can be differentiated by the methodology followed to make recommendations. Some RS systems are based on data filtering; other systems uses a combination of filtering and AI/ML.

However, what connects these systems is the large volume of data coming from different sources.

In most cases, making recommender systems requires the following four steps:

1. Data Collection

Data is the basic element for making a recommendation system.

These data sets are collected from various sources based on user behaviour and their selection criteria. There are multiple parameters involved in the data collection process.

2. Data Storage

Once you’ve collected sufficient data, you need to find a way to store it.

Data must be stored (in a) securely as you do not want to lose the most valuable element of the recommendation system. Structured query language (SQL) and not only structured query language (NoSQL) databases are common storage solutions; but in most cases NoSQL is preferred for large volumes of data. (Also read: What are some of the key issues to consider in a big data storage strategy?)

3. Data Processing

In this step, data is processed and ordered based on some parameters like characteristics, type and sources. The purpose of this stage is to prepare the data and facilitate the filtering process.

4. Applying Filters

This is the most important step, where the actual recommendation is made.

Here, processed data is used in different filters to extract the most appropriate recommendations. These filters are made based on different algorithms.

AI- and ML-Powered Recommender Systems

The future of recommendation engines will be ruled by artificial intelligence- and machine learning-based systems.

That’s because AI-based recommender systems are more personalized and can reach potential customers easily. They can also make recommendations faster than traditional systems, saving the time and effort required to search a product, increasing conversion and, consequently, propelling business growth. (Also read: Why are machine learning rational agents so important to retail applications?)

What makes AI-based recommender systems different? Well, most of the recommendation approaches discussed so far are based on linear rules, which means they follow simple mathematical algorithms. As a result, they always work the same way regardless of user behavior. By contrast, AI-based recommendation systems follow non-linear rules — instead using machine learning algorithms to suggest the most appropriate products/content.

The two key aspects of AI based systems are customization and automation.


Customization is the key to success for any recommendation system; and it’s much more accurate in AI-based systems than in traditional recommender systems.

That’s because machine learning algorithms are very efficient at analyzing data and predicting suggestions. Plus, AI- and ML-based systems are constantly learning, allowing them to improve over time and generate even better outputs.


Automation plays another important role in AI-based recommendation solutions. Organizations can automate the mechanical steps necessary in the recommendation process to generate better results in less time.

In automated AI-based recommender systems, the AI- and/or ML-based systems perform real-time data analysis and the automation takes care of the rest.

Data science is playing a very important role in developing AI-based recommendation systems.


As the digital age has evolved, users have a wide range of options which results in fierce competition in the field of digital marketing. Thus, recommendation systems are helping users acquire the products they desire.

The key to success for recommender systems is understanding customers’ minds and their inclinations towards particular products and content. Traditional recommendation systems are, to some extent, successful at recommending items. But AI- and ML-powered systems have the potential to make them even more efficient and personalized. (Also read: The Way We Buy Now: The ABCs of BNPL.)


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…