Last Updated on by Kumar Raja

Understanding The Recommender Systems In Artificial Intelligence

The rapid advancements in the technology of Artificial Intelligence are proving to be highly beneficial for the business of all types. The online e-commerce business has also transformed a lot with the advent of AI-driven smart business solutions. By making use of AI-driven recommendation engines, e-commerce & other content based online platforms are now able to deliver their users with recommendations that would exactly match their choices.

Amazons product recommendations, Netflix movie or web series recommendations, Facebook friend suggestions are the best practical applications of recommendation systems that are currently in use. If you are curious about building an AI-driven recommender system of your own then join us for our advanced AI Training In Hyderabad program by experts.

Now, let’s get a clear idea about what exactly the recommendation systems are

Recommender Systems or Recommender Engine-

A recommender system can simply be interpreted as an advanced information filtering system. This application of this system is based on the analysis that is made on the users’ data related to their search history, behavioural pattern on the website, cookies and more.  By analyzing this data, the recommender system comes up with accurate item suggestions that would surely be in accordance with the users’ interest.

Role Of AI & Machine Learning in Recommendation Systems-

To come up with accurate recommendations to the users’ recommendation systems rely on advanced algorithms in Machine Learning in addition to Artificial Intelligence. ML algorithms contribute to accuracy in the system. The behavioural performance of these algorithms in recommender systems varies based on the data that is fed to the system.

The algorithms that are used in recommendation systems can be classified into two categories namely collaborative filtering and content-based filtering. The recommendation systems that are in use today make use of both of these filtering techniques. Content-Based Filtering is related to similarity in different products whereas Collaborative Filtering is related to the way how users interact with different products.

So undoubtedly, data will be playing a crucial role in these recommender systems along with Machine Learning & AI algorithms. These recommender systems will not only keep the users engage but will also be leading to improved sales. Get to know more in-depth about the functioning of the recommender systems by joining for our Kelly Technologies advancedAI training program. 

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