Repurchase Behavior and Time Interval Prediction Fused with Temporal Attenuation Characteristics

Repeated consumption behavior is a common phenomenon in many recommendation scenarios, such as e-commerce repurchases and interest point punching; this behavior includes both the possibility and timing of a repurchase. This study mainly focuses on the prediction of a single factor (prediction of either the possibility or timing of repurchase). However, this does not address the specific questions of when and what to buy again. The main challenges associated with this type of problem are as follows: the types of repurchase items are very diverse, different items have different purchase cycles, and repurchase behavior is often sparse; these challenges make prediction very difficult. Furthermore, repurchase behavior includes two dimensions—time and items—and using the information from these two dimensions for prediction purposes is also difficult. A solution to these problems is explored from the perspective of user-personalized dynamic attenuation characteristics and a fusion model based on repurchase behaviors and time intervals. First, the user's interest in an item decreases over time and recent behavior has a stronger potential correlation with repurchase behavior; therefore, a modeling item sequence is proposed to obtain the user expression vector, and the information of the neighboring sequence is used to solve the problem of repurchase behavior sparsity. Second, by reasonably designing the neural network module, the user's personalized repurchase cycle and the item's repurchase cycle are captured, and the information fusion problem of time and items is solved. A large number of experiments are conducted on multiple public datasets, the results of which confirm that the model developed in this study is superior to existing benchmark models related to this study.