Survey on a Trust-Aware System for Personalized User Recommendations in Social Networks

Abstract: Trust is an important part in a social network from security point of view. Online video sharing systems is the most popular and provide features that allow users to post a video in a web page. These features provide opportunities for a user to introduce polluted content into the system. Spammersmay post an unrelated video aiming at increasing the likelihood of the responsebeing viewed by a larger number of users. Multimedia recommendation system recommends video based on the user behavior which reduces network overhead and speed up the recommendation process. The proposed approach can recommend desired services with high precision, high recall and low response delay. To avoid the explosion of networkoverhead, user-behavior-based clustering is performed. If unrelated content is displayed in our web page, then we spam that video content. If more users spam all video content from same provider, then the provider will be deleted from server.

Keywords: social networks, recommendation, personalization, trust.