| A Review for Recommender System Models and Deep Learning |
| Paper ID : 1047-ICCI2021 (R1) |
| Authors: |
|
Fainan Nagy AbdAllah El Sisi *1, Asmaa Haroun2, Hatem Abdul-Kader3, Arabi Keshk4 155555 2information system department, Faculty of computers and Informations, Menofia University, El Menofia, Egypt 3Information System Department, Faculty of Computers and Information, Menoufia University El Menoufia, Egypt 4Computer Science dept. Faculty of Computers and Information, Menofia University, Egypt |
| Abstract: |
| In the era of big data and the advancement in technology accelerated, the need to make a choice from a huge amount of various alternatives, and a large amount of online information, is a time consuming and very tedious task to evaluate all these online features. Recommendation systems are an enormous solution and have caught the attention of researchers and companies recently to solve information overload problem, it can process a large amount of data and help the user to make a decision making. In this paper we introduce an overview for traditional recommendation systems models, the recommender system challenges, common deep learning techniques, and the new research directions on this field. The main contribution of this paper is the proposed model which is inspired from [1] in which we trying to integrate sentiment analysis model, we will implement using deep learning, with their model, trying to improve their recommendation model performance. |
| Keywords: |
| recommender system, deep learning, challenges, RS future research directions, proposed model. |
| Status : Paper Accepted |
