Hybrid Recommendation Systems in News Media using Probabilistic Graphical Models

Description

A typical undertaking of recommender frameworks is to enhance customer experience through prior implicit feedback, by providing relevant content from time to time. These systems actively track different sorts of user behavior, such as buying pattern, watching habits browsing activity etc., in order to model user preferences. Unlike the much more extensively explored explicit feedback, we do not have any direct input from the users regarding their preferences. Where understanding the content is important, it is non-trivial to explain the recommendations to the users.

When a new customer comes to the system it is very difficult to provide relevant recommendations to the customer by traditional state-of-art collaborative filtering based recommendation systems, where content-based recommendation does not suffer from this problem. On the other hand, content-based recommendation systems fail to achieve good performance when the user profile is not very well defined, where collaborative filtering does not suffer from this problem. So, there is a need to combine the power of these two recommendation systems and create a hybrid recommendation system which can address this problem in a more effective and robust way. Large media and edtech companies in emerging markets are using a version of this approach.

Presentation Video