This article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning in Learning Networks. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system with...

The use of relevant metrics of software systems could improve various software engineering tasks, but identifying relationships among metrics is not simple and can be very time consuming. Recommender systems can help with this decision-making process; many applications have utilized these systems to improve the performance of their applications. To investigate the potential benefits of recommen...

Recommender systems have become essential in many web sites, especially in the e-commerce area; however, they are not extended enough in some domains. In this work, a recommender system for TV series is presented due to the increasing interest for this kind of products. The system implements a methodology that deals with the most important problems of recommender systems.

Recommender systems are used extensively now-a-days for various web-sites such as for providing products suggestions based on customers’ purchase history and searched product keywords. Current recommendation system approaches lack of a high degree of stability. Diversification of prediction is also important feature of recommender system. Having displayed same set of results every time may incr...

Recommender systems have been shown to help users find items of interest from among a large pool of potentially interesting items. Influence is a measure of the effect of a user on the recommendations from a recommender system. Influence is a powerful tool for understanding the workings of a recommender system. Experiments show that users have widely varying degrees of influence in ratings-base...

We investigate a new class of software for knowledge discovery in databases (KDD), called recommender systems. Recommender systems apply KDD-like techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-Commerce today. We extend previously studied KDD models to incorporate customer interaction so these mo...

Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed article...

Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the s...

In the age of information overload, recommender systems help users to find what they like, but in return they can affect users interests. Recommender systems can narrow users options to a limited community of items or informations. Our goal is to develop tools to investigate the effect of recommender systems on the social networks. Netflix Prize winner algorithm is an example of good recommende...

Recommender systems use algorithms to provide users product recommendations. Recently, these systems started using machine learning algorithms because of the progress and popularity of the artificial intelligence research field. However, choosing the suitable machine learning algorithm is difficult because of the sheer number of algorithms available in the literature. Researchers and practition...