In this paper, we present new extensions to traditional approaches to recommender systems by making recommender systems support data warehousing capabilities. In particular, we propose recommender systems to work in multidimensional settings as opposed to the traditional twodimensional user/item environments. We also propose recommender systems to support rich profiling and OLAP capabilities.

In this era when every aspect of society is accelerating, people are always seeking improvement to stay competitive in their careers. E-learning systems fit into the ever challenging situation and provide learners with remote learning opportunities abundant resources. Facing numerous resources online, users need support deciding which course take, thus recommender applied personalized services ...

Systems able to suggest items that a user may be interested in are usually named as Recommender Systems. The new emergent field of Recommender Systems has undoubtedly gained much interest in the research community. Although Recommender Systems work well in suggesting books, movies and items of general interest, many users express today a feeling that the existing systems don’t actually identify...

Recommender Systems apply machine learning and data mining techniques to filter undetected information and can predict whether a user of a system would like a given resource based on his/her interests and preferences. To date a number of recommendation algorithms have been proposed, where Collaborative Filtering (CF) and Content-Based Filtering (CBF) are the two most famous and adopted recommen...

Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two si...

Reputation systems are employed to provide users with advice on the quality of items on the Web, based on the aggregated value of user-based ratings. Recommender systems are used online to suggest items to users according to the users, expressed preferences. Yet, recommender systems will endorse an item regardless of its reputation value. In this paper, we report the incorporation of reputation...

This article proposes a framework of Web miningbased recommender systems for e-commerce. Building on clustering analysis of data involving Web usage, content and structure, the author demonstrates how to provide users with effective recommender services according to the mining results obtained by recommender engine. Finally, the author reaches his conclusion of the advantages and practicalities...