Collaborative Filtering with Fine-grained Trust Metric

Similarity-based collaborative filtering systems are vulnerable to the data sparsity, cold-start, and robustness problems. Computational trust models are promising alternative solutions to alleviate these problems by replacing similarity metric with trust metric. However, they often have some shortages that rely on users' explicit trust statements. A fine-grained model computing trust from user ratings is more reasonable and gets more nonintrusive for average users. We propose a novel trust-based recommendation model for this purpose. Experiments on a large real dataset show that the proposed model has better performance in terms of MAE, Coverage, and F-metric than the conventional collaborative filtering model.