A recommendation engine is a system that, based on data analysis, proposes products, services, and information to users. Regardless, the recommendation can be based on a range of criteria, including the user’s history and the behaviour of identical users.
A recommendation engine can help with click-through rates, conversions, and other important KPIs. It has the ability to boost consumer loyalty and retention by improving the user experience. Take, for example, Netflix. Instead of having to go through hundreds of movie titles, Netflix provides a far more concentrated selection of products that user’s are likely to enjoy. This technology helps you save time and enhances your overall user experience. Thanks to this feature, Netflix was able to cut cancellation rates, saving the company almost a billion dollars each year.
Recommendation engines must understand more about users in order to be effective with their recommendations. As a result, the information they collect and incorporate is critical to the process. Information regarding explicit interactions, such as prior activity, ratings, reviews, and other personally identifiable information, such as gender, age, or investment goals, can be included.
Content-based filtering, collaborative filtering, and Knowledge Based Systems are the three primary types of methods used in recommendation systems.
- Content Based Filtering:
Filtering based on a single user’s interactions and preferences is known as content-based filtering. The metadata gathered from a user’s history and interactions is used to provide recommendations. Recommendations, for example, will be based on known trends in a user’s choices or behaviours. Products and services will be provided to you depending on your preferences or points of view. A recommendation like “items that are related to this” on e-Commerce giant website Amazon is a good example of this method.
- Collaborative Filtering:
Collaborative filtering is another popular approach. Collaborative filtering casts a much broader scope, collecting information from many other users’ behaviours to provide recommendations for you. This method generates recommendations based on the likes or situations of other users. For example, by using their behaviours and actions to suggest product recommendations to you or to determine how one product would complement another. The term “next buy” is commonly used to describe recommendations. Collaboration-based filtering is typically more accurate than content-based filtering.
- Knowledge-based system
Knowledge-based systems are those in which recommendations are made based on a user’s demands and a level of subject experience and knowledge. There are rules in place that define the context for each recommendation. This, for example, could be factors that determine when a certain financial product, such as a trust, is advantageous to the consumer.
Measure of Recommendation System:
Measures of accuracy and coverage are examples of traditional measuring approaches.
- Coverage defines the number of products or users for whom the system can offer suggestions.
- Accuracy is defined as the percentage of right recommendations out of all potential recommendations.
For example, accuracy and coverage is the preferred measurement of any model.
Let’s use Netflix as an example again. Netflix’s recommendation system is at its heart. A recommendation mechanism is used to find more than 80% of the TV series that users view on the platform. The method is unusual in that it does not look at broad genres, but rather at subtle connections within the text. The goal is to help viewers discover shows they would not have selected otherwise.
Finally, recommendation systems are becoming more integrated into all aspects of human life and decision-making. This phenomenon is being replicated in other industries, particularly in consumer-facing businesses, where information overload, growing customer demands, and cost-cutting are driving an increase in the use of recommendation engines.