Quality (philosophy)

World Wide Web

Process (computing)

Engineering

Philosophy

Aerospace engineering

Computer security

Cold start (automotive)

Social trust

Operating system

Computer science

Social network (sociolinguistics)

Filter (signal processing)

Business

Internet privacy

Collaborative filtering

Social media

Epistemology

Social capital

Sociology

Data science

Artificial intelligence

Recommender system

Knowledge management

Class (philosophy)

Computer vision

Key (lock)

Social science

International Journal on Advanced Science Engineering and Information Technology

Recommendation systems or recommender system (RSs) is one of the hottest topics nowadays, which is widely utilized to predict an item to the end-user based on his/her preferences primary. Recommendation systems applied in many areas mainly in commercial applications. This work aims to collect evidence of utilizing social network information between users to enhance the quality of traditional recommendation system. It provides an overview of traditional and modern approaches used by RSs such as collaborative filter (CF) approach, content-based (CB) approach, and hybrid filter approach. CF is one of the most famous traditional approaches in RSs, which is facing many limitations due to the lack of information available during a performance such as Cold start, Sparsity and Shilling attack. Additionally, this content focused on the role of incorporating a trust relationship from the social network to enhance the weaknesses of CF and achieve better quality in the recommendation process. Trust-aware Recommendation Systems (TaRSs) is a modern approach proposed to overcome the limitations of CF recommendation system in a social network. The trust relationship between users can boost and enhance CF limitations. Many researchers are focusing on trust in the recommendation system but fewer works are highlighting the role of trust in the recommendation system. In the end, limitations, and open issues of the current picture of the recommendation system come across.

The Role of Trust to Enhance the Recommendation System Based on Social Network