Authors : Hanafi , Nanna Suryana and Abdul Sammad Bin Hasan Bashari
Abstract: Recommender systems are an important technique for creating effective communication between users and retailers in E-commerce services. Good communication and easy to find the product will increase marketing target. On the other hand, will give significant effect to achieving the target value of transactions between users and retailers in online shopping industry. Recommender systems have begun to implement in the mid-90’s and many researchers have given the effort to enhance some weaknesses of existing system stronger also because there are many changes of social paradigm and E-commerce industry. One of the models is quite successful recommender system is collaborative filtering, but there is a major drawback of this model is in dealing with the cold start and sparsity of data. The problem rise when new user and new item is coming. There are many solution strategies to handle the problem. In these paper, we show many possible solutions include in there exploring algorithm model and exploiting information from implicit and explicit information that comes from social media, a feature of product/item and user profiles.
Hanafi , Nanna Suryana and Abdul Sammad Bin Hasan Bashari, 2020. Addressing Sparsity Data and Cold Start Problem on Collaborative Filtering Recommender System for E-Commerce: A Review. Journal of Engineering and Applied Sciences, 15: 2025-2037.