Abstract
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 provide 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. Keywords: Ask-To-Rate Technique, Cold-Start, Collaborative Filtering Recommender System, Demographic Data, Model Based Approach, Social Network Analysis
Highlights
With an increasing market size, electronic commerce is a driving force for a business to enable a firm or individual for online shopping or marketing over an electronic network, typically the internet
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
The enough information is not available for a new item or user, the recommender system suffers into the item/user cold-start problem
Summary
With an increasing market size, electronic commerce is a driving force for a business to enable a firm or individual for online shopping or marketing over an electronic network, typically the internet. Because it reduces the transaction cost, low energy cost and provides access to the global market. The enough information is not available for a new item or user, the recommender system suffers into the item/user cold-start problem.
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