Safety is one concern that hinders the acceptance of ridesharing in general public. Several studies have been conducted on trust issue recent years to relieve this concern. The introduction systems provides a pragmatic approach solving problem. In study, we will develop trust-aware recommender system decision model generate recommendations for drivers and passengers. requirements both sides, pa...

It is a common and useful task in a web of trust to evaluate the trust value between two nodes using intermediate nodes. This technique is widely used when the source node has no experience of direct interaction with the target node, or the direct trust is not reliable enough by itself. If trust is used to support decision-making, it is important to have not only an accurate estimate of trust, ...

Recommender systems are important building blocks in many of today’s e-commerce applications including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated many researchers to focus on designing systems that produce personalized recommendations in accordance with the available contextual information of us...

There have been various definitions, representations and derivations of trust in the context of recommender systems. This article presents a recommender predictive model based on collaborative filtering techniques that incorporate a fuzzy-driven quantifier, which includes two upmost relevant social phenomena parameters to address the vagueness inherent in the assessment of trust in social netwo...

Recommender systems (RS) are designed to assist users by recommending them items they should appreciate. User based RS exploit users behavior to generate recommendations. As a matter of fact, RS performance fluctuates across users. We are interested in analyzing the characteristics and behavior that make a user receives more accurate/inaccurate recommendations than another. We use a hybrid mode...

Recommender systems provide personalized recommendation for their users. These systems are still needed to be optimized to provide more effective recommendations. In some models the context of user and the item is considered during the recommendation process so that it would be possible to make a better estimation of the user's rating. In this article context aware recommender models are addres...

In view of information overflow on the web, the use of recommender systems seems to be an appropriate means by which to organize information that targets preferences. The purpose of this article is to present a novel model explaining the satisfaction with recommender websites integrating emerging influential factors such as trust, exploratory browsing, and personal factors. Three recommender sy...

recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...