Recommendation Engines

How do people select the products to purchase when they are shopping from an e-commerce website? Shoppers online look for products that match their requirements and check the user reviews if they are confused about selecting an item. Some people think of shopping as a necessity, and some do it for pleasure. For both kinds of shoppers, online stores have come up with highly advanced recommendation tools that help in selecting the right product and making better choices. It is similar to having an automated form of shop counter, which suggests the products based on queries.

What Are Recommendation Engines? 

Similar to search engines that give search results related to the query input by the user, recommendation engines suggest several products to the buyers to help make them a decision. These are data filtering tools that use complex algorithms and data to recommend the relevant items to the buyer. It also shows the related products and similar matches which helps in upselling the items. As the number of buyers online have increased dramatically in the last decade, it presents an opportunity for online sellers to target the buyers by showing the relevant products.

The crux of the recommendation engine from the business perspective is to increase the revenue of the organization by boosting online sales. Amazon and Netflix are the prime examples using the recommendation engines at full potential. Statistics reveal 200 million unique buyers visit Amazon every month, and the giant online seller sold more than 175 million products on the prime day sale in 2019. With such an influx of customers, it is imperative that the seller shows the products that are most relevant to the customers, and this is done by the advanced recommendation engines in place. Amazon does it by keeping an eye on the browsing history of the buyer and showing the recommendations on the email campaigns and advertisement on the pages visited by the prospective buyer. The reviews and ratings are used to display products with a greater average.

Type of Recommendation Engines 

Online businesses are using three types of recommendation engines to analyze the customer’s choices and recommend products. 

  • Collaborative filtering 
  • Content-Based Filtering 
  • Hybrid Recommendation Systems 

Collaborative Filtering

The engine analyzes the behaviour, activities, and choices of the customer and makes recommendations based on its learning. The engine can predict the choices of the customers and present an assortment of items to the customer. The advantage of the engine is, it doesn’t rely on the content and can accurately predict complex items like movies without understanding the items itself. It is based on the idea that people will like things based on their preferences and behaviour. It tries to match the users with similar preferences and present the product which is liked by the user with the same preferences.

Content-Based Filtering 

The engines based on content-based filtering look through the profile of the users for preferred choices and match it with the item description to suggest the products. The algorithm works to recommend similar products liked by the user in the past. The engine will recommend similar movies and songs to the user, which were searched and watched by the user previously. There are some limitations in the engine which restricts the use to only a select few categories. The engine cannot replicate the user preferences to different categories, like suggesting movies based on the choice of the news articles read by the user is out of the scope of the engine. 

Hybrid Recommendation Systems 

The system combines the capabilities of both collaborative and content-based engines. The combination can help in predicting the product based on the likes, preferences, choices, and previous purchases of the user. Netflix is a perfect example of a Hybrid system that recommends series and movies based on the characteristics of already watched movies and the search queries done by the user. It utilizes the capabilities of collective and collaborative engines.

Using Recommendation Engines amidst the Global Pandemic (COVID-19) 

The current situation of global lockdown has put normal life in shackles and has taken a toll on online businesses. Recommendation engines can be a powerful tool in this time as it can help the people in suggesting the staples items of necessity that should be purchased. Online stores providing deliveries can alter the recommendation engine model to suggest essential items over luxuries. This will help the buyers in making the right decisions at this time of the pandemic. 

There is no doubt that the shopping experience is enhanced by the suggestive models. Recommendation Engines are evolving with time and helping the customers in decision making and thereby boosting the sales of the e-commerce organizations. The sellers are trying to imbibe cognitive computing methods in the engine’s predictive model to get better suggestions and take the quality of recommendations to the next level. This will help in retaining and providing quality service to the customer.