Predictive Model of Live Shopping Interest Degree Based on Eye Movement Characteristics and Deep Factorization Machine

In the live broadcast process, eye movement characteristics can reflect people's attention to the product. However, the existing interest degree predictive model research does not consider the eye movement characteristics. In order to obtain the users' interest in the product more effectively, we will consider the key eye movement indicators. We first collect eye movement characteristics based on the self-developed data processing algorithm fast discriminative model prediction for tracking (FDIMP), and then we add data dimensions to the original data set through information filling. In addition, we apply the deep factorization machine (DeepFM) architecture to simultaneously learn the combination of low-level and high-level features. In order to effectively learn important features and emphasize relatively important features, the multi-head attention mechanism is applied in the interest model. The experimental results on the public data set Criteo show that, compared with the original DeepFM algorithm, the area under curve ( AUC) value was improved by up to 9.32％.