A Recommendation system is an information service system that connects users and items {“Movies”, “Books”, “Music”}. The system has implemented based on hybrid approach of collaborative filtering and context based filtering. It helps users to discover new items of its interests. The system can be highly improved by making use of caching mechanisms. Getting important keywords from the feedback provided by the user for an item and utilizing these keywords in context based filtering.
The main problem is here that the cold-start problem, as Collaborative filtering uses correlation matrix which uses most of the available ram on the device. Lack of datasets. Cannot handle fresh item sets. If the user matrix or item matrix is changed the cosine similarity matrix has to be recalculated. Previous user history is required or data for the items is required.
