Add to ORKGTo improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Appro...
Automated recommender systems have played a more and more important role in marketing and ever incre...
A remarkable growth in quantity and popularity of online social networks has been observed in recent...
The existing recommendation algorithms often rely heavily on the original score information in the u...
Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which...
Relationships between users in social networks have been widely used to improve recommender systems....
In order to alleviate the pressure of information overload and enhance consumer satisfaction, person...
Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity o...
A discovered algorithm based on the dynamic trust relations of users in a social network system (SNS...
Trust-aware recommender systems have attracted much attention recently due to the prevalence of soci...
In Web-based social networks (WBSN), social trust relationships between users indicate the similarit...
In Web-based social networks (WBSN), social trust relationships between users indicate the similarit...
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 ...
Recommended system is beneficial to e-commerce sites, which provides customers with product informat...
With the increasing popularity of social network services, social network platforms provide rich and...
Automated recommender systems have played a more and more important role in marketing and ever incre...
A remarkable growth in quantity and popularity of online social networks has been observed in recent...
The existing recommendation algorithms often rely heavily on the original score information in the u...
Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which...
Relationships between users in social networks have been widely used to improve recommender systems....
In order to alleviate the pressure of information overload and enhance consumer satisfaction, person...
Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity o...
A discovered algorithm based on the dynamic trust relations of users in a social network system (SNS...
Trust-aware recommender systems have attracted much attention recently due to the prevalence of soci...
In Web-based social networks (WBSN), social trust relationships between users indicate the similarit...
In Web-based social networks (WBSN), social trust relationships between users indicate the similarit...
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 ...
Recommended system is beneficial to e-commerce sites, which provides customers with product informat...
With the increasing popularity of social network services, social network platforms provide rich and...
Automated recommender systems have played a more and more important role in marketing and ever incre...
A remarkable growth in quantity and popularity of online social networks has been observed in recent...
The existing recommendation algorithms often rely heavily on the original score information in the u...