The paper presents a recommendation-based approach for knowledge resources in Communities of Practice of E-learning (CoPEs). The proposed approach is based on the hybrid semantic information filtering (IF), integrating the content-based filtering, the collaborative filtering and the ontology-based filtering approaches. The main idea is to apply a multi-level filtering, where three dimensions ha...

Collaborative filtering is an important technique of information filtering, commonly used to predict the interest of a user for a new item. In collaborative filtering systems, this prediction is made based on user-item preference data involving similar users or items. When the data is sparse, however, direct similarity measures between users or items provide little information that can be used ...

The process of filtering information or patterns using techniques involving collaboration among multiple agents or data sources is known as collaborative filtering [14]. Applications of collaborative filtering typically involve very large data sets. Techniques of Collaborative filtering have been applied to many different fields such as sensing and monitoring data in mineral exploration, enviro...

With the development of personalized services, collaborative filtering techniques have been successfully applied to the network recommendation system. But sparse data seriously affect the performance of collaborative filtering algorithms. To alleviate the impact of data sparseness, using user interest information, an improved user-based clustering Collaborative Filtering (CF) algorithm is propo...

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the Simple Bayesian Classifier, and apply our model to two variants of the collaborative fi...

A recommender system utilizes in general an information filtering technique called collaborative filtering. To improve prediction quality, collaborative filtering needs reinforcements such as utilizing useful attributes of the items as well as a more refined neighbor selection. In this paper we present that the recommender systems that utilizing the attributes of the items in collaborative filt...

In this paper we introduce the idea of using a reliability measure associated to the predictions made by recommender systems based on collaborative filtering. This reliability measure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recommender system. We will also show a m...

Coordination is critical in distributed collaborative analysis of multidimensional data. Collaborating analysts need to understand what each person has done and what avenues of analysis remain uninvestigated in order to effectively coordinate their efforts. Although visualization history has the potential to communicate such information, common history representations typically show sequential ...