Paulo Nunes speaks about the benefits of Artificial Intelligence and Machine Learning in everyday live at prestigious Frauenhofer Institute.
Paulo Nuno gaves some interesting insights on the usage and practical implementation of Recommender Systems during this years “Startup Corporate Exchange” at Frauenhofer Institute. A Recommender Engine is a subclass of information filtering systems that seeks to predict the “rating” or “preference” that a user would give to an item. According to Mckinsey, cross-selling techniques based on recommendations increase sales by 20% and profits by 30%.
The biggest market leaders have built in Recommender Systems
One of the reasons why digital platforms such as Amazon, Netflix or Linkedin are so successful are their built in Recommender Systems . Users are served with content that actually interests them. Based on various factors, users can be recommended products from predictive models. This helps to increase company performance through personalization.
How do Recommender Systems work?
A good overview about this topic can be found in the video from Stanford University below:
The different types of Recommender Systems are described in our full PDF “Increase Revenue and Performance through Recommendation Systems”
Ask the Expert
Do you have further questions or want to build a customized soltution? Don’t hesitate and contact us!
About the Author: OnFore
Leave A Comment
You must be logged in to post a comment.