How to Evaluate Recommender Systems
Imagine we have built an item-based recommender system to recommend users movies based on their rating history. And now we want to assess how our model will perform. Is it any good at actually recommending users movies that they will like? Will it help users find new and exciting movies from a plethora of movies available in our system? Will it help improve our business? To answer all these questions (and many others) we have to evaluate our model.
Create Audiences in Firebase Analytics
One of the main features of any analytics is the ability to create audiences. Firebase Analytics audiences let you segment your users in ways that are important to your business. Audiences can be used to:
Understand How Item Based Collaborative Filtering Works
Recommendation systems have been around us for quite some time now. Youtube, Facebook, Amazon, and many others provide some sort of recommendations to their users. This not only helps them show relevant products to users but also allows them to stand out in front of competitors.
Personalize User Experience with Machine Learning
Introduction The goal of this analysis was to identify different user groups based on the deals they have availed, using a discount app, in order to re-target them with offers similar to ones they have availed in the past.