Recommender Systems Challenges and Solutions Survey

Abstract

Today’s Recommender system is a relatively new area of research in machine learning. The recommender system’s main idea is to build relationship between the products, users and make the decision to select the most appropriate product to a specific user. There are four main ways that recommender systems produce a list of recommendations for a user – content-based, Collaborative, Demographic and hybrid filtering. In content-based filtering the model uses specifications of an item in order to recommend additional items with similar properties. Collaborative filtering uses past behavior of the user like items that a user previously viewed or purchased, In summation to any ratings the user gave those items rate and similar conclusions made by other user’s items list. To predicts items that the user may find interesting. Demographic filtering is view user profile data like age category, gender, education and living area to find similarities with other profiles to get a new recommender list. Hybrid filtering combines all three filtering techniques. This paper introduces survey about recommendation systems, techniques, challenges the face recommender systems and list some research papers solve these challenges.