Add to ORKGRecommender Systems are becoming increasingly indispensable nowadays since they focus on solving the information overload problem, by providing users with more proactive and personal-ized information services. Typically, recommender systems are based on Collaborative Filtering, which is a technique that au-tomatically predicts the interest of an active user by collecting rating information from other similar users or items. Due to their potential commercial values and the associated great re-search challenges, Recommender systems have been extensively studied by both academia and industry recently. However, the data sparsity problem of the involved user-item matrix seriously affects the recommendation quality. Many ex-isting approaches to r...
108 pagesOver the last few decades, recommender systems have become important in affecting people's ...
Recommender systems have become an important research area since the emergence of the first research...
This paper proposes a number of studies in order to move recommender systems beyond the traditional ...
Background. In this article, we look at the key advances in collaborative filtering recommender syst...
AbstractIn this era of web, we have a huge amount of information overload over internet. To extract ...
The tremendous growth in the amount of available information and the number of visitors to Web sites...
Recommender systems can be seen everywheretoday, having endless possibilities of implementation. How...
Recommender systems apply data mining techniques and prediction algorithms to predict users ’ intere...
Abstract—Recommender systems are often used to provide useful recommendations for users. They use ...
The goal of a recommender system is to generate relevant recom-mendations for users. It is an inform...
In this thesis three different types of reccommender systems were compared: baseline predictor, coll...
In this thesis we report the results of our research on recommender systems, which addresses some of...
The massive growth of information these days has created the need for information filtering techniqu...
Traditional approaches to recommender systems have often focused on the collaborative filtering prob...
As the Internet becomes larger in size, its information content threatens to be-come overwhelming. T...
108 pagesOver the last few decades, recommender systems have become important in affecting people's ...
Recommender systems have become an important research area since the emergence of the first research...
This paper proposes a number of studies in order to move recommender systems beyond the traditional ...
Background. In this article, we look at the key advances in collaborative filtering recommender syst...
AbstractIn this era of web, we have a huge amount of information overload over internet. To extract ...
The tremendous growth in the amount of available information and the number of visitors to Web sites...
Recommender systems can be seen everywheretoday, having endless possibilities of implementation. How...
Recommender systems apply data mining techniques and prediction algorithms to predict users ’ intere...
Abstract—Recommender systems are often used to provide useful recommendations for users. They use ...
The goal of a recommender system is to generate relevant recom-mendations for users. It is an inform...
In this thesis three different types of reccommender systems were compared: baseline predictor, coll...
In this thesis we report the results of our research on recommender systems, which addresses some of...
The massive growth of information these days has created the need for information filtering techniqu...
Traditional approaches to recommender systems have often focused on the collaborative filtering prob...
As the Internet becomes larger in size, its information content threatens to be-come overwhelming. T...
108 pagesOver the last few decades, recommender systems have become important in affecting people's ...
Recommender systems have become an important research area since the emergence of the first research...
This paper proposes a number of studies in order to move recommender systems beyond the traditional ...