Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Ubiquitous RFID: Where are we?
Information Systems Frontiers
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
| Hi-index | 0.00 |
Modeling interest of a user for services recommendation and friendship between users is the major activity of social networks. The information used by social networks such as user profiles is unfortunately easy to be faked and misled by the users, which often results in poor service recommendation and friendship prediction. In this paper, we propose a propagation model that integrates the emerging Web of Things (resource/services networks) and social networks together so that better service recommendation and friendship prediction can be achieved by considering interactions between people and things.