Abstract. In this paper we propose a method that can be used to avoid the problem of sparsity in recommendation systems and thus to provide improved quality recommendations. The concept is based on the idea of using trust relationships to support the prediction of user preferences. We present the method as used in a centralized environment; we discuss its efficiency and compare its performance with other existing approaches. Finally we give a brief outline of the potential application of this approach to a decentralized environment
International audienceRecommender Systems are widely used to achieve a pre-selection of items among ...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
Recommender systems help Internet users quickly find information they may be interested in from an e...
Increasing availability of information has furthered the need for recommender systems across a varie...
Recommender Systems allow people to find the resources they need by making use of the experiences a...
In order to alleviate the pressure of information overload and enhance consumer satisfaction, person...
This PhD thesis addresses the following problem: exploiting of trust information in order to enhance...
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing ...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
Recommender Systems (RS) have emerged as an important response to the so-called information overload...
This PhD thesis addresses the following problem: exploiting of trust information in order to enhance...
Abstract—It is difficult for the users to reach the most appropriate and reliable information/item f...
Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity o...
Recommender systems are one of the recent inventions to deal with ever growing information overload ...
To alleviate the issue of data sparsity in collaborative filtering (CF), a number of trust-aware rec...
International audienceRecommender Systems are widely used to achieve a pre-selection of items among ...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
Recommender systems help Internet users quickly find information they may be interested in from an e...
Increasing availability of information has furthered the need for recommender systems across a varie...
Recommender Systems allow people to find the resources they need by making use of the experiences a...
In order to alleviate the pressure of information overload and enhance consumer satisfaction, person...
This PhD thesis addresses the following problem: exploiting of trust information in order to enhance...
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing ...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
Recommender Systems (RS) have emerged as an important response to the so-called information overload...
This PhD thesis addresses the following problem: exploiting of trust information in order to enhance...
Abstract—It is difficult for the users to reach the most appropriate and reliable information/item f...
Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity o...
Recommender systems are one of the recent inventions to deal with ever growing information overload ...
To alleviate the issue of data sparsity in collaborative filtering (CF), a number of trust-aware rec...
International audienceRecommender Systems are widely used to achieve a pre-selection of items among ...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
Recommender systems help Internet users quickly find information they may be interested in from an e...