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.
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