Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Social and behavioural media access
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Exploring social influence via posterior effect of word-of-mouth recommendations
Proceedings of the fifth ACM international conference on Web search and data mining
Tag-aware recommender systems: a state-of-the-art survey
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Mobile social networks: state-of-the-art and a new vision
International Journal of Communication Systems
An experimental study on implicit social recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Recommender system by grasping individual preference and influence from other users
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
AnchorMF: towards effective event context identification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A new user similarity model to improve the accuracy of collaborative filtering
Knowledge-Based Systems
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Social recommendation, that an individual recommends an item to another, has gained popularity and success in web applications such as online sharing and shopping services. It is largely different from a traditional recommendation where an automatic system recommends an item to a user. In a social recommendation, the interpersonal influence plays a critical role but is usually ignored in traditional recommendation systems, which recommend items based on user-item utility. In this paper, we propose an approach to model the utility of a social recommendation through combining three factors, i.e. receiver interests, item qualities and interpersonal influences. In our approach, values of all factors can be learned from user behaviors. Experiments are conducted to compare our approach with three conventional methods in social recommendation prediction. Empirical results show the effectiveness of our approach, where an increase by 26% in prediction accuracy can be observed.