Our AI-powered system generates personalized news suggestions using a probabilistic prediction model created by analyzing reader behavior, holding their attention for longer and saving time. As a result, website traffic rises by 5 to 8%, and the material is improved based on reader preferences. Deep learning and NLP models were used in the Python-based system, which was then saved on Apache Cassandra.
Business Challenge:
News websites aim to increase their readers and traffic on their websites. However, they fail at captivating their reader’s attention and interest for longer time span.
Solution:
A probabilistic prediction model can be generated by studying the liking, disliking and digital footprints data gathered from records of previous readers.
AI Engine Results:
By using the results of probabilistic prediction model the liking and disliking of the readers can be predicted and with the aid of those predictions recommendations can be made to readers according to their interest.
Results:
Readers interest can be captivated for longer time span. It will save their time in searching for news and there would be fewer chances for them to miss on news of their interest.
Client Success:
The web traffic over the website would increase by 5-8%. The client would have more systemized data depending on liking and disliking of readers, and this can aid in improving the content of the website.
Technology Stack:
Language: Python
Models: Deep Learning Models, Natural Language Processing
Storage: Apache Cassandra