A multi-behavior recommendation method exploring the preference differences among various behaviors

User behavior data has been widely used in recent research of recommendation systems. Existing work usually utilize only single behavior instead of multi-behavior. However, there are various typed of user behaviors in practical scenarios. For example, there are view, purchase, add-to-cart and add-to-favorite behaviors in a real-world e-commerce platform. For recommendation systems, using more user information is better for exploiting intrinsic user characteristics, as the single behavior records are usually too sparse to conduct preference mining. Therefore, multi-behavior based recommendation methods are increasingly emphasized by researchers. In this paper, we propose a novel framework (FPD) for utilizing multi-behavior data: generating embedding and building training losses. We compute an additional supplementary score by capturing user's preference difference among behaviors, instead of merely using initial embedding to obtain a ordinary prediction score. For better optimization model parameters, we use various behaviors to build multiple training losses. We optimize the loss function of the non-sampling strategy and set personal positive weights for each user. Experimental results on two real-world datasets demonstrates that our model outperformed the state-of-the-art methods in terms of various evaluation measurements. Furthermore, extensive ablation experiments and visualization analysis are conducted to verify the effectiveness of the proposed idea and to further explain the principle of the proposed method proposed.