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...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
These days, due to growing the e-commerce sites, access to information about items is easier than pa...
Recommendation systems, based on collaborative filtering, offer a means of sifting through the enour...
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...
Abstract. Past evidence has shown that generic approaches to recommender systems based upon collabor...
A remarkable growth in quantity and popularity of online social networks has been observed in recent...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivit...
Recommender systems are one of the recent inventions to deal with ever growing information overload ...
Similarity metrics play a key role in case-based reasoning: an effective retrieval step is a premise...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
These days, due to growing the e-commerce sites, access to information about items is easier than pa...
Recommendation systems, based on collaborative filtering, offer a means of sifting through the enour...
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...
Abstract. Past evidence has shown that generic approaches to recommender systems based upon collabor...
A remarkable growth in quantity and popularity of online social networks has been observed in recent...
In this paper we examine an advanced collaborative filtering method that uses similarity transitivit...
Recommender systems are one of the recent inventions to deal with ever growing information overload ...
Similarity metrics play a key role in case-based reasoning: an effective retrieval step is a premise...
Similarity-based recommender systems suffer from significant limitations, such as data sparseness an...
These days, due to growing the e-commerce sites, access to information about items is easier than pa...
Recommendation systems, based on collaborative filtering, offer a means of sifting through the enour...