Add to ORKGIn collaborative filtering recommender systems, users cannot get involved in the choice of their peer group. It leaves users defenseless against various spamming or “shilling” attacks. Other social Web-based systems, however, allow users to self-select trustworthy peers and build a network of trust. We argue that users self-defined networks of trust could be valuable to increase the quality of recommendation in CF systems. To prove the feasibility of this idea we examined how similar are interests of users connected by a self-defined relationship in a social Web system, CiteuLike. Interest similarity was measured by similarity of items and meta-data they share. Our study shows that users connected by a network of trust exhibit significantly...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivit...
Social Networks have dominated growth and popularity of the Web to an extent which has never been wi...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, there is little room for users to get involved in th...
In collaborative filtering recommender systems, there is little room for users to get involved in th...
In collaborative filtering recommender systems, there is little room for users to get involved in th...
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 ...
Abstract. Past evidence has shown that generic approaches to recommender systems based upon collabor...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
A remarkable growth in quantity and popularity of online social networks has been observed in recent...
Recommendation systems or recommender system (RSs) is one of the hottest topics nowadays, which is w...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivit...
Social Networks have dominated growth and popularity of the Web to an extent which has never been wi...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, users cannot get involved in the choice of their pee...
In collaborative filtering recommender systems, there is little room for users to get involved in th...
In collaborative filtering recommender systems, there is little room for users to get involved in th...
In collaborative filtering recommender systems, there is little room for users to get involved in th...
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 ...
Abstract. Past evidence has shown that generic approaches to recommender systems based upon collabor...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
A remarkable growth in quantity and popularity of online social networks has been observed in recent...
Recommendation systems or recommender system (RSs) is one of the hottest topics nowadays, which is w...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivit...
Social Networks have dominated growth and popularity of the Web to an extent which has never been wi...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...