{"id":49278,"date":"2022-09-07T00:00:00","date_gmt":"2022-09-07T00:00:00","guid":{"rendered":"https:\/\/www.techopedia.com\/how-recommender-systems-are-changing-e-commerce\/"},"modified":"2024-02-13T07:53:56","modified_gmt":"2024-02-13T07:53:56","slug":"how-recommender-systems-are-changing-e-commerce","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/2\/31445\/trends\/big-data\/how-recommendation-systems-are-changing-our-lives","title":{"rendered":"How Recommender Systems Are Changing E-Commerce"},"content":{"rendered":"

Until very recently, skilled salespeople needed to be present in physical stores to recommend products to customers. But not anymore.<\/p>\n

These days, recommender systems<\/a> 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.<\/p>\n

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

What is a Recommendation System?<\/span><\/h2>\n

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

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<\/a>. In a sense, these peer opinions are like manual recommendations.<\/p>\n

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<\/a>: the most popular recommendation technology to this day.<\/p>\n

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<\/a>, and they are mainly impacting e-commerce<\/a> and online shopping. (Also read: <\/strong>4 New Technologies Making Waves in the E-commerce Sector<\/strong><\/a>.)<\/strong><\/p>\n

How do Recommender Systems Work?<\/span><\/h2>\n

The core of recommendation system is based on recommendation approaches.<\/p>\n

The most common approaches followed in recommendations are:<\/p>\n

Collaborative Filtering<\/h3>\n

Collaborative filtering<\/a> 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.<\/p>\n

The similarity between individuals can be tracked by studying things like their browsing patterns, search options, purchase history and ratings.<\/p>\n

Collaborative filtering is the most common approach recommendation systems follow.<\/p>\n

Content-Based Filtering<\/h3>\n

Content-based filtering<\/a> focuses on consumers individually.<\/p>\n

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.”<\/p>\n

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.<\/p>\n

Knowledge-Based Filtering<\/h3>\n

In knowledge-based<\/a> 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<\/a> and makes recommendations based on that.<\/p>\n

Demographic Filtering<\/h3>\n

This types of system recommends based on the user’s demographic data<\/a>.<\/p>\n

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.<\/p>\n

Community-Based Filtering<\/h3>\n

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.<\/p>\n

Hybrid Filtering Systems<\/h3>\n

Hybrid filtering combines multiple filtering approaches to recommend the most appropriate products\/content.<\/p>\n

The benefit of this system is to maximize the benefits of each filtering system while downplaying their shortcomings.<\/p>\n

Popular Recommendation Systems<\/span><\/h2>\n

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

Some notable services that rely on recommender systems include:<\/p>\n