TY  - JOUR
T1  - Addressing Sparsity Data and Cold Start Problem on Collaborative Filtering Recommender
System for E-Commerce: A Review
AU - , Hanafi AU - Suryana, Nanna AU - Sammad Bin Hasan Bashari, Abdul 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 9
SP  - 2025
EP  - 2037
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.2025.2037
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.2025.2037
KW  - Recommender system
KW  -collaborative filtering
KW  -social recommender
KW  -cold start
KW  -sparsity data
KW  -E-commerce
AB  - 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.
ER  -