A recommender system, which might assist in providing clients with new information and a better experience, is becoming increasingly popular this era of modernization. Recommender systems are often used by various platforms to provide products consumers, may also help improving product sales. Additionally, the system essential academic domains. It common for users take while find access materia...

Many recommender systems might be part of an e-commerce or multi functional system (or portal) where various information about users, products/documents, social networks, and different types of user feedback about products/documents are available. This paper exploits the heterogeneous information a recommender system might collect to make the most appropriate recommendations. We propose a new P...

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrus...