A Review and Classification of Multi-Criteria Recommender Systems

Recommender systems (RSs) are personalization tools that gives recommendations for items to users by exploiting various methods. Conventional collaborative filtering (CF) based RSs provide suggestions to users based on overall rating of items which is not an efficient procedure as users in system may have different choices on different criteria. So, multicriteria recommender systems (MCRS) came into existence as an extension of traditional CF based RSs. MCRS recommends items to users based on number of criteria. Recommending products to users from the vast catalog is still a challenge for researchers. This paper presents a review of some significant work in the area of multi-criteria recommender system. After a brief introduction, we present review of existing methods categories according to heuristic and model based approach, and some of the popular approaches are classified into different sets such as recommendation fields, research problem, data mining and machine learning techniques. Insights and possible future work in the area of MCRSs are also discussed.