Personalized recommender systems have emerged as a powerful method for improving
both the content of customers and the profit of providers in e-business environment …

Recommender systems aim to assist web users to find only relevant information to their
needs rather than an undifferentiated mass of information. Collaborative filtering (CF) …

Nearest-neighbor collaborative filtering (CF) algorithms are gaining widespread acceptance
in recommender systems and e-commerce applications. User ratings are not expected to be …

Recommender Systems are used to mitigate the information overload problem in different
domains by providing personalized recommendations for particular users based on their …

Recommender Systems are gaining widespread acceptance in e-commerce applications to
confront the information overload problem. Collaborative Filtering (CF) is a successful …

Personalized recommendation systems are becoming increasingly popular with the
evolution of the Internet, and collaborative filtering is one of the most important technologies …

Recommender systems have achieved widespread success for e-commerce companies.
Significant growth of customers and products poses key challenges for recommender …

Collaborative filtering (CF) technique has been proved to be one of the most successful
techniques in recommender systems. Two types of algorithms for collaborative filtering have …

Recommender is a personalized service in the adaptive information system, and it can
provide personalized information according to individual information needs. As one of the …

Recommender systems employ prediction algorithms to provide users with items that match
their interests. The collaborative filtering (CF) is the most popular system and the two of the …