TWCF: Trust Weighted Collaborative Filtering based on Quantitative Modeling of Trust

Trust-based collaborative filtering methods exploit trust information to alleviate cold-start and data sparsity prob-lems and improve recommendation performance. Instead of modeling trust itself, most existing approaches rely on trust connections sourced from online social networking platforms. Typically, users engage with products on rating platforms and interact with friends on social networking platforms, exhibit-ing distinct behavior patterns. Therefore, simply aggregating information and misinterpreting user preferences may lead to information misuse and recommendation bias. To overcome these limitations, this paper introduces three trust measurement models to capture and quantify trust relationships directly from user-provided ratings. Utilizing the trust-weighted scheme, we propose a hybrid collaborative filtering approach called Trust Weighted Collaborative Filtering (TWCF). Experiments on three real-world datasets show that the trust information extracted from rating data and the trust-weighted scheme can significantly improve the performance of original neighborhood collaborative filtering. TWCF achieves an average improvement of 7.12% in prediction accuracy over the trust-based collaborative filtering baselines. Furthermore, the interpretability and scalability of TWCF provide opportunities for further improvement by incorporating more appropriate trust measurement models.

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