Repurchase Behavior and Time Interval Prediction Fused with Temporal Attenuation Characteristics

doi:10.19678/j.issn.1000-3428.0070046

Abstract

Abstract:

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.

Key words: recommended algorithm, temporal attenuation, collaborative filtering, interpretability, k-Nearest Neighbor (k-NN)

摘要：

重复消费行为在许多推荐场景中是一种普遍的现象, 如电商复购、兴趣点打卡等。重复消费行为包括复购可能性和复购时机两个因素, 现有的工作主要关注单个因素的预测, 无法有效解决何时复购何物这类具体的问题。此类问题的主要挑战是复购商品类型非常多样, 不同商品有不同的购买周期, 而复购行为往往比较稀疏, 给预测带来极大困难。另外, 复购行为包括时间和商品两个维度, 如何利用这两个维度的信息进行预测也是一个挑战。针对上述挑战, 从用户个性化的动态衰减特性这一角度探索解决方法, 建立复购行为及时间间隔的联合预测模型。首先, 根据用户对某商品的兴趣随着时间衰减以及近期行为与复购行为具有更强的潜在关联关系, 建模商品序列以获得用户表达向量, 同时利用邻居序列的信息以解决复购行为稀疏性问题; 其次, 设计神经网络模块, 捕获用户的个性化复购周期和商品复购周期, 解决时间和商品两个维度的信息融合问题。在多个公开数据集上的大量实验结果表明, 该模型优于现有相关的基准模型。

关键词: 推荐算法, 时序衰减, 协同过滤, 可解释性, k近邻

WEN Wen, ZHONG Yanhong, HAO Zhifeng. Repurchase Behavior and Time Interval Prediction Fused with Temporal Attenuation Characteristics[J]. Computer Engineering, 2026, 52(4): 111-121.

温雯, 钟宴宏, 郝志峰. 融合时序衰减特性的复购行为及时间间隔预测[J]. 计算机工程, 2026, 52(4): 111-121.

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Figures/Tables 12

Fig.1 Framework of prediction model

Fig.2 Hierarchical attenuation

Fig.3 Nearest neighbor collaborative prediction

Fig.4 Influence of two attenuation coefficients on the model effect

Fig.5 Influence of sliding window size on model effect

Fig.6 Item weight heat map

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