The issue of trust is important in recommender systems. These systems are typically described in terms of perceived reliability of the recommender coupled with a content quality perspective. However, most studies do not address the complete user context and psychological environment of a recommender system. This environment and context are described here by three primary areas of consideration,...

Past evidence has shown that generic approaches to recommender systems based upon collaborative filtering tend to poorly scale. Moreover, their fitness for scenarios supposing distributed data storage and decentralized control, like the Semantic Web, becomes largely limited for various reasons. We believe that computational trust models bear several favorable properties for social filtering, op...

Traditional Recommender Systems (RS) do not consider any personal user information beyond rating history. Such information, on the other hand, is widely available on social networking sites (Facebook, Twitter). As a result, social networks have recently been used in recommendation systems. In this paper, we propose an efficient method for incorporating social signals into the recommendation pro...

In order to predict and recommend what users want, users’ information is required, more required improve the performance of recommender system. As IoT devices smartphones have made it possible know user’s context, context-aware systems emerged preferences by considering context. A system uses contextual such as time, weather, location preferences. However, a are not always same in given They ma...

A Recommender System (RS) is a composition of software tools and machine learning techniques that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Numerous approaches have been proposed for providing recommendations. Certainly, recommendation systems have an assortment of properti...