Recommender Process Based on Trust-Distrust Factor for Signed Social Networks

Recommender process have gained popularity in recent years due to their effectiveness in addressing the issue of information overload. It is nonetheless susceptible to several intrinsic problems, such as cold starting and data scarcity. Numerous studies show that leveraging data from social networks is a highly successful tactic to deal with such issues. The interaction between users based on their conduct and social links is also taken into consideration in research on the recommendation process that integrates social interactions, in addition to the user's preferences for the product. Social network relationships that involve both trust and distrust have not grabbed much attention. Our research finding suggests the methods of integrating the trust and distrust social relationships using machine learning in order to enhance the collaborative filtering recommendation algorithm, which combined the users’ trust and distrust social relationships and effectively reduced the sparseness in the signed social networks. The experimental results shows that the recommended methodologies outperform state-of-the-art algorithms based on trust-distrust using machine learning.