Abstract:
"
E-commerce recommender systems can assist clients with discovering what they
wanted or new items they may be liked on. To consistently upgrade user trust in the
site, improve page visits also, stay time, and in particular, increment net product
esteem, it is critical to comprehend furthermore, catch the significant data covered up
in the information the recommendation system needs the attributes of the users and
items to do the best recommendation system.
In here we are going to go through the fashion datasets in amazon which was mostly
used datasets in fashion industry. In here we are going to make not only user gave
rating to particular item-based interaction we going to have multiple ways of
interaction to do the proper recommendation for in this case we are going to use the
knowledge graph for making different types of interactions and for knowledge graph
we are going to use neo4j graph database.
And also, we going to use different type of the approach of current technologies to do
the neural collaborative filtering technologies to do the better recommendation with
deep learning and we are going to do the data filtering approaches to separate the
items in different types to make the recommendation system greatly, because we are
using these things to improve the data quality in recommendation."
