One of the crucial components behind the working of a product recommendation engine is the recommender function, which considers specific information about the user and predicts the rating that the user might assign to a product. 

Having the ability to predict user ratings, even before the user has provided one, makes recommender systems a powerful tool.

There are many problems solved by machine learning, but making product recommendations is a widely recognized application of machine learning. There are mainly three essential types of recommendation engines –

1. Collaborative Filtering

The collaborative filtering method is based on collecting and analyzing information based on behaviors, activities, or user preferences and predicting what they will like based on the similarity with other users. The prediction is done using various predictive maintenance machine learning techniques.

For example, if user X likes Tennis, Badminton, and Golf while user Y likes Tennis, Badminton, and Hockey, they have similar interests. So, there is a high probability that X would like Hockey and Ywould enjoy Golf. It is how collaborative filtering is done.

The two types of collaborative filtering techniques are –

• User-User collaborative filtering
• Item-Item collaborative filtering

2. Content-Based Filtering

Content-based filtering methods are mainly based on the description of an item and a profile of the user’s preferred choices. In content-based filtering, keywords are used to describe the items, whereas a user profile is built to state the type of item this user likes.

For example, if a user likes to watch movies such as Mission Impossible, then the recommender system recommends movies of the action genre or movies of Tom Cruise.

3. Hybrid Recommendation Systems

Hybrid Recommendation engines are essentially the combination of diverse rating and sorting algorithms. For instance, a hybrid recommendation engine could use collaborative filtering and product-based filtering in tandem to recommend a broader range of products to customers with accurate precision.

Netflix is an excellent example of a hybrid recommendation system as they make recommendations by:

• Comparing the watching and searching habits of users and finding similar users on that platform, thus making use of collaborative filtering
• Recommending such shows/movies which share common characteristics with the ones rated highly by the user. It is how they make use of content-based filtering.

Among the key advantages of recommendation, engines include –

Enhance Sales & Average Order Value

One of the excellent methods to increase your revenue and average order value (AOV) is to encourage your website visitors to add recommended products and offerings at the checkout page.

Recommendation systems allow you to drive much higher conversions and enhance average order value. You can bring multiple data sets (historical data, real-time visitor behavior, and third-party insights) into a recommendation algorithm using a recommendations engine. These data sets can then deliver relevant recommendations in real-time and allow customers to engage with your brand in real-time.

Helps You Deliver Customized And Relevant Content

One of the most efficient ways for any brand to meet customer expectations is to build customized and relevant content. Recommended system allows brands to personalize the customer experience and make suggestions for the items that make the most sense to them.

Deliver A Consistent Brand Experience

Recommendation engine AI can be key to creating a consistent brand experience by simply drawing data from various channels. It allows you to optimize your omnichannel customer experience and make customers feel part of an ongoing journey instead of starting afresh with each interaction. 

Drives Website Traffic

Using a recommendation engine allows you to bring targeted traffic to your website. The recommender system can achieve this with specifically targeted blasts and personalized email messages.