A Survey and Classification on Recommendation Systems

In today’s modern world, the data is growing exponentially and the traditional systems are not able to fulfil the user’s requirements. To fulfil the needs of the users, various companies like Amazon, Netflix, etc. are using recommender systems which recommend content or various type of data on the basis of the user’s previous activities and interactions with the system. In the recommender system, mainly three approaches are present, i.e., content-based, collaborative filtering and knowledge-based approaches. Due to their wide applicability, recommender systems have become an area of active research and in this context, this paper furnishes a survey and comparative discussion of existing approaches. The survey draws a conclusion on how different recommendation techniques are cooperating with today’s growing technology trends and also discusses the challenges faced by them.