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...

In this paper, we investigate recommender systems from a network perspective and investigate recommendation networks, where nodes are items (e.g., movies) and edges are constructed from top-N recommendations (e.g., related movies). In particular, we focus on evaluating the reachability and navigability of recommendation networks and investigate the following questions: (i) How well do recommend...

Recommendation process plays an important role in many applications as W.W.W. Recommender systems uses the users, items, and ratings information to predict how other users will like a particular item. An important response to the information overload problem is provided by the recommender system, as it presents users more personalized and practical information services. In the recommender syste...

Accounting for missing ratings in available training data was recently shown [3, 17] to lead to large improvements in the top-k hit rate of recommender systems, compared to state-of-the-art approaches optimizing the popular rootmean-square-error (RMSE) on the observed ratings. In this paper, we take a Bayesian approach, which lends itself naturally to incorporating background knowledge concerni...

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...

Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full ...

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...