The recommendation engine will add customer satisfaction. Also, the good thing here is it is applicable in many things such as home page, emailing campaign, product details, and many more.
So how does it works?
Introduction About The Recommendation Engine
Technologies almost supply in today’s digital world. And one of the most powerful that technology created is a recommendation engine.
The recommendation engine is like a system. It performs as the following
- filtering information from the system. This system is composed of machine learning and algorithms.
- it addresses the challenge in the e-commerce space.
So as a result, it can save a lot of our precious time. Instead, we scroll up and down looking for what we need.
Moreover, the recommendation engine is almost everywhere.
You want products there is Amazon. A movie recommendation there is Netflix.
Also, you want to listen to music? Here are youtube and Spotify. So you have a lot of choices in everything you need.
Wow. That is so great about the recommendation engine.
Different Characters Of Recommendations
So here are the three important characters of recommendations.
- Collaborative filtering
- Hybrid Recommendation Systems
- Content-Based Filtering
Collaborative Filtering
The key benefit of collaborative filtering is the approach. It does not machine analyzable content.
However, it is capable of the precise recommending complex.
Moreover, there are various types of collaborative filtering algorithms:
- User-user – The algorithm is very useful. However, it takes a lot of time and also resources. This kind of filtering needs counting every customer pair information and that is time-consuming.
- Item-item – It is very likely to the past algorithm, however rather than locating a customer look-alike, we try locating items look alike.
- Other simpler algorithms – Some procedures like market basket analysis, is usually do not have high predictive capability than the algorithms explained earlier.
Hybrid Recommendation Systems
It is the collaboration of collaborative and content-based recommendation. This can be more effective.
The approach here is implementing in making content-based and collaborative-based predictions. By adding these two you can have a unification approach.
Content-Based Filtering
These methods are based on the description items and profile of the user. Also, keywords are used for describing the items.
Recommendation Engine Works
So it works because of machine learning on the computer engine. This is to make a product recommendation.
But here is the list of processes data for the recommendation engine.
- Collection of data – So of course you need to gather first all the data. These data can be implicit or explicit.
The implicit data are order history or return. Then the explicit is consists of data input by users such as ratings or comments.
- Storing The Data – So the more data the more algorithms. Also, it can result in better recommendations.
- Analyzing the data – There are various ways to analyze the data.
- Real-time systems – The system is required to give in-the-moment recommendations.
- Batch Analysis – It demands process data periodically. This also works fine to send an email at a later date.
- Near-real-time – The system works best for storing recommendations.
