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首页> 外文期刊>Electronic commerce research and applications >Handling sequential pattern decay: Developing a two-stage collaborative recommender system
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Handling sequential pattern decay: Developing a two-stage collaborative recommender system

机译：处理顺序模式衰减：开发两阶段协作推荐系统

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摘要

This study proposes a sequential pattern based collaborative recommender system that predicts the customer's time-variant purchase behavior in an e-commerce environment where the customer's purchase patterns may change gradually. A new two-stage recommendation process is developed to predict customer purchase behavior for the product categories, as well as for product items. The time window weight is introduced to produce sequential patterns closer to the current time period that possess a larger impact on the prediction than patterns relatively far from the current time period. This study is the first to propose time-decaying sequential patterns within a collaborative recommender system. The experimental results show that the proposed system outperforms the traditional collaborative system using a public food mart dataset and a synthetic dataset.
机译：这项研究提出了一种基于顺序模式的协作推荐系统，该系统可以预测在电子商务环境中客户的购买方式可能会逐渐变化的客户的时变购买行为。开发了一个新的两阶段推荐过程，以预测产品类别以及产品项目的客户购买行为。引入时间窗口权重以产生更接近当前时间段的顺序模式，该顺序模式比相对远离当前时间段的模式对预测的影响更大。这项研究是第一个在协作推荐系统中提出时间衰减序列模式的方法。实验结果表明，该系统优于使用公共食品市场数据集和综合数据集的传统协作系统。

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