In an ever increasing paced world we want everything to happen fast and easy. Our attention span and our frustration fuse are both getting shorter. Nowadays we kind of expect from web services to know us intimately and to know our desires without us telling them. This is where recommendation systems kick in. They save us time and makes us feel like a web service has exactly what we need. Thus in time when users have access to millions of news sources it is vital to navigate the user to the ones most interesting to him.
Net Info has provided data with historical visits of articles per user. The data consists of user ID, URL, timestamp, article title and article views.. The idea is to create a model that can recommend the next best article for a user. Here besides taking account of the user reading history, one can also take account for the user history of each article thus learning from the history of people with similar interests.
Each model will be benchmarked against real data from the next day regarding recommending the next best article. In addition bonus points would be given to teams using Tensorflow.