Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Social recommender systems for web 2.0 folksonomies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Personalised and dynamic trust in social networks
Proceedings of the third ACM conference on Recommender systems
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
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Traditional recommender system research often explores customer, product, and transaction information in providing recommendations. Social relationships in social networks are related to individuals' preferences. This study investigates the product recommendation problem based solely on people's social network information. Taking a kernel-based approach, we capture consumer social influence similarities into a graph random walk kernel and build SVR models to predict consumer opinions. In experiments on a dataset from a movie review website, our proposed model outperforms trust-based models and state-of-the-art graph kernels.