This study proposes a novel collaborative filtering framework which integrates both
subjective and objective information to generate recommendations for an active consumer …

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

With the rapid development and application of the mobile Internet, huge amounts of user
data are generated and collected every day. How to take full advantages of these ubiquitous …

Recommender systems are information filtering systems designed to resolve the problem of
information overload by automatically recommending items of interest to particular users …

Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a
user's needs based on data coming from similar users, are becoming increasingly popular …

Collaborative filtering (CF) algorithm has been popularly used for recommender systems in
both academic and practical applications. A general CF system compares users based on …

With the rapid growth of wireless technologies and mobile devices, there is a great demand
for personalized services in m-commerce. Collaborative filtering (CF) is one of successful …

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 …

In the area of recommender systems, collaborative filtering is widely used technique for
recommending appropriate items to a user based on the available ratings given by similar …

Collaborative filtering (CF) either memory based or model based, has been emerged as an
information filtering tool that provides effective recommendations to users utilizing the …