Our client is an international media organization with over 28 years of rich history. They have their presence in USA, Europe, Asia, Africa and Australia.
The Client broadcasts inspirational content on their platform. Their platform allows publishing of even those content which are independently produced. All of their content is intended to uplift, encourage and assist viewers.
Collect user data, to comprehend the data and to profile the users based on their choices on viewing the content.
And, for this, client wanted us to build an AI Recommendation Engine which can help them collect user data and understand it. By doing this, client wanted to cater to the specific needs of the users, intending to provide content that will aid in the growth of the user.
What is AI based Recommendation Engine and how does it help?
AI enables recommendation engines to quickly and to-the point tailor each customer’s recommendations to their requirements and preferences. Online searching is becoming more effective thanks to AI because it now generates suggestions based on the customers’ visual preferences rather than product details. AI-powered recommendation engines assist users in finding items or material that they might not otherwise notice. Because of this, recommendation engines are crucial to the success of companies like Amazon, Facebook, YouTube, etc.
For anticipating the needs of a particular user, our recommender system considers the following:
The primary goal of all AI search strategies is to enhance the comprehension of long, complicated queries and deliver the right answer even when the input data is inaccurate or partial. Systems without AI uses simple algorithms which makes expected recommendation predictive and limited.
After testing various AI based algorithms and filtering systems such as Item based, User Based, Content Based, Collaborative, Hybrid filtering models, team concluded that Hybrid Filtering system is the best solution for our recommendation system.
Hybrid Recommendation System
Technologies and tools
Python v3.10, MongoDB v3.6, PHP v7.4, MySql v10, HTML, Bootstrap v4.3