School of Economics and Management, Beijing Jiaotong University, China.
Comput Intell Neurosci. 2022 Jul 21;2022:7273728. doi: 10.1155/2022/7273728. eCollection 2022.
E-commerce systems often collect data that clearly express user preferences without considering the remaining negative cases, which gives rise to the hidden semantic problem. In this paper, we improve the original hidden semantic model and propose an intergroup effect model that incorporates users' historical browsing behavior, user type, and browsing content; by adopting the weighting and add weighting factors, we can predict users' preferences for different products more accurately and match the candidate products with users' current behaviors, so as to give more reasonable and effective product recommendation results; by adding the group effect model of user group and product group, we can achieve more accurate prediction of user preferences and make the recommendation more reasonable and effective. The research shows that the hidden semantic method based on intergroup effects information is better than other basic methods at a certain identified evaluation stage. In practice, users' purchasing preferences change with time, and using a hidden semantic method based on intergroup effects recommendation can effectively improve the recommendation quality of e-commerce recommendation systems.
电子商务系统通常会收集明确表达用户偏好的数据，而不考虑其余的负面情况，这就产生了隐藏的语义问题。在本文中，我们改进了原始的隐藏语义模型，并提出了一种群组效应模型，该模型结合了用户的历史浏览行为、用户类型和浏览内容；通过采用加权和添加加权因子，我们可以更准确地预测用户对不同产品的偏好，并将候选产品与用户当前的行为相匹配，从而给出更合理有效的产品推荐结果；通过添加用户组和产品组的群组效应模型，我们可以更准确地预测用户的偏好，并使推荐更合理有效。研究表明，基于群组效应信息的隐藏语义方法在某个特定的评估阶段优于其他基本方法。在实际应用中，用户的购买偏好会随时间而变化，而使用基于群组效应推荐的隐藏语义方法可以有效地提高电子商务推荐系统的推荐质量。
