Collaborative Filtering Recommender System: Comparative Survey on Cold-Start Issue

S. Vairachilai
2018 Indian Journal of Science and Technology  
Objectives: To analyze the issue of cold-start (user cold-start and item cold-start) in Collaborative Filtering Recommender System (CFRS) and to compare its solution with various approaches are summarized in this paper. Methods/Statistical Analysis: The manuscript discussed about the cold-start issue in which the recommender system cannot recommend items to the new user because no ratings made by the new user (user cold-start) as well as for the newly added items, the system cannot be able to
more » ... ovide recommendations to the user because the system has no ratings for the newly added item (item cold-start). The solutions for cold-start issue are analyzed based on the model based approach, demographic data, ask-to-rate technique, and Social Network Analysis (SNA). Findings: The comparative review of the aforementioned approaches provides the detail about how to implement the model based approach, how to collect the demographic data from the new user, how to apply the ask-to-rate technique and how to make use of the SNA concept to solve the cold-start issue in CF recommender system. Application: The recommender system on Amazon helps the user to purchase books, Compact Disks (CDs), Netflix helps the user to choose CDs to purchase/rent and Epinions, helps the users to decide to purchase based on user reviews. potential to solve the information overload problem. The recommender system task is to recommend items and also help the user in selecting/purchasing items from an overwhelming set of choices. The collaborative filtering recommender system is one of the most successful approaches in an e-commerce website. Collaborative filtering is a method that provides personalized recommendations, based on preferences expressed by a set of users and calculates the similarity between customer preference ratings to identify like-minded customers and predict their product preferences. Although the collaborative filtering recommender system successful, it undergoes a major issue such as
doi:10.17485/ijst/2018/v11i20/116392 fatcat:2rav5q432jhulpxrsalsp5fkau