A Review Paper on Recommender System Type’s and Evaluation Metrices - Singla, Anshu; Gupta, Geeta; Rathore, Mohit (IJRDO-Journal of Applied Science, 2016)
A very simple definition of Recommender Systerm is “Any system that produces personalized recommendations as output or helps the user in guiding in a personalized way to get interesting or useful objects from a large collection of useful options.”This Review paper presents the study in the field of recommender systems and describes the current generation of recommendation methods w h i c h i s usually divided into the three main categories: content-based recommender system, collaborative recommender system,Utility Based ,Knowledge Based and hybrid recommendation techniques.This paper prescribes different terms applicable to an even broader range of applications for improvement in accuracy of recommendation algorithms. The research carried out has focused on improving the accuracy of recommender systems. In this paper, we propose that the recommender system should move beyond the conventional accuracy criteria and take some other criteria into account, such as coverage, diversity, serendipity, scalability, adaptability, risk, novelty and so on. These extensions include, among others, improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multi-criteria ratings, and provision of more flexible and less intrusive types of recommendations.