Trust-aware collaborative filtering recommendation in reputation level
Hong Gen Zhou, Qing Li, Fang Zhou · 2017
We propose a novel method based on user's double identities in the context of social network which people can consume information as well as generate content. We acquire relationship, which we call trust, between users from users' activities that performed on the related items (i.e. resource) that authors have published. Then we improve users' ratings on items with their relationship with authors. Moreover, both reputation on users and items are computed to incorporate into our recommendation model to improve predictive accuracy. Compared with the classic user-based collaborative filtering recommendation, the experiment shows that our method is better in predictive accuracy.