Engine Recommendation In Brief

Recently, Engine Recommendation has become everywhere! Most of the biggest brands are using it, including Facebook, Amazon, Netflix, and much more.?

But what is the engine recommendation and how does it work??

Engine Recommendation

It is a type of filtering data tool that is used by machine learning algorithms to recommend the most relevant item to a specific user. It works on finding the patterns in consumer behavior data.?

In other words, the recommender system has lots of techniques and algorithms that are used to recommend the most relevant items to the user based on what he has searched on before. Like Youtube, Netflix, articles, etc.?

The items are ranked according to the most relevant for the user.?

What Are The Types Of Engine recommendations?

1. ?Engine Recommendation has three main types, which are collaborative filtering, content-based filtering – and a hybrid of the two.

Let’s dig into more and know each type of them.?

Collaborative Filtering

It focuses on collecting the data of the user like his behavior, preferences, activities, etc. then analyzing this data to predict what the user will like based on their similarity to other users.?

To calculate those similarities, collaborative filtering is using something called matrix formula. And there is an advantage for collaborative filtering, which is that it doesn’t need to analyze or understand the content type, whether it is an article or video or whatever. It picks the items based on what they learned about this user?

Under the umbrella of collaborative filtering, there are 2 subgroups: memory-based and model-based methods.

The memory-based method is the simplest as it uses no model whatsoever, as it assumes that prediction can be made on pure memory of past data wherever it was.?

The Model-based method is different, as it is always assumed some of the underlying models, and try to make sure that whatever predictions come out will fit that model well.

Content-Based Filtering?

Content-Based Filtering works on the method that if a user likes a particular item then he will love this other item too. To make a recommendation, the algorithm uses the profile of the user’s preference (Type of product, genre, color, and product length) to know the similarity of the items.?

That’s why when you sign up for many websites, they ask for your gender and your birth date. It is data to helo their systems predict better.?

Hybrid Model?

The hybrid Recommendation model engine can look at the collaborative filtering data and the content-based filtering data, that why it can outperform the past two models.?

?In Talent Supplier, we support new technologies and always looking for developers who love learning and working in it