FluidRating: A Time-Evolving Rating Scheme in Trust-based Recommendation Systems Using Fluid Dynamics

The goal of a trust-based recommendation system is to predict unknown ratings based on the ratings expressed by trusted friends. However, most of the existing work only considers the ratings at the current time slot. In real life, a user receives the influence of different opinions sequentially; accordingly, his opinion evolves over time. We propose a novel rating prediction scheme, FluidRating, which uses fluid dynamics theory to reveal the time-evolving formulation process of human opinions. The recommendation is modeled as fluid with two dimensions: the temperature is taken as the "opinion/rating," and its volume is deemed as the "persistency," representing how much one insists on his opinion. When new opinions come, each user refines his opinion through a round of fluid exchange with his neighbors. Opinions from multiple rounds are aggregated to gain a final prediction; both uniform and non-uniform aggregation are tested. Moreover, Three sampling approaches are proposed and examined. The experimental evaluation of a real data set validates the feasibility of the proposed model, and also demonstrates its effectiveness.