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 personalizedrecommendations as output or helps the user in guiding in a personalized way to get interesting or useful objectsfrom a large collection of useful options.”This Review paper presents the study in the field of recommender systemsand describes the current generation of recommendation methods w h i c h i s usually divided into thethree main categories: content-based recommender system, collaborative recommender system,Utility Based,Knowledge Based and hybrid recommendation techniques.This paper prescribes different terms applicable to aneven broader range of applications for improvement in accuracy of recommendation algorithms. The research carriedout has focused on improving the accuracy of recommender systems. In this paper, we propose that therecommender system should move beyond the conventional accuracy criteria and take some other criteria intoaccount, such as coverage, diversity, serendipity, scalability, adaptability, risk, novelty and so on. These extensionsinclude, among others, improvement of understanding of users and items, incorporation of the contextualinformation into the recommendation process, support for multi-criteria ratings, and provision of more flexible andless intrusive types of recommendations.