Movie Endorsement to Deliver Top-N Recommendation for User using CoFiTor Framework

The objective of top N recommendation is to recommend to each user a minor set of N items from a huge group of items based on past dataset on the user's preferences. It provides appropriate recommendations for a user to improve the shopping experience. With the help of this approach users can choose items quickly from a shorter list of suitable products. A lot of current video streaming website recommendation systems do not take individual user's shopping behavior into account. In this research study, a transfer to rank approach that mockups a user's behaviors by simulating the user's shopping processes is considered because this approach has been shown to be better than other state-of-the-art methods. A novel and comprehensive recommendation framework termed coarse-to-fine transfer to rank (CoFiToR), which is a major extension of the most recent work known as transfer to rank, is used to achieve this method (ToR). The proposed approach able to produce more relevant product recommendations for each and every user. As an improvement, the product description can correlate the behavior data in all three stages to generate the recommendation list.