Reviewing Cluster Based Collaborative Filtering Approaches

Local Approaches for Collaborative Filtering

QoS-based Web Service Recommendation using Popular-dependent Collaborative Filtering

Since, most of the organizations present their services electronically, the number of functionally-equivalent web services is increasing as well as the number of users that employ those web services. Consequently, plenty of information is generated by the users and the web services that lead to the users be in trouble in finding their appropriate web services. Therefore, it is required to provi...

The collaborative filtering recommendation based on SOM cluster-indexing CBR

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lesseni...

Sparsity Eliminated Collaborative Filtering Using Cluster-based Weighting

Method of Collaborative Filtering Based on Uncertain User Interests Cluster

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. The suggestions provided are aimed at supporting their users in various decision-making processes, such as what items to buy, what music to listen, or what news to read. In the paper, we introduce ...