基于用户隐性反馈行为的下一个购物篮推荐

下一个购物篮推荐是当前电子商务领域中极其重要的一项任务,传统的下一个购物篮推荐方法主要分为时序推荐模型和总体推荐模型。这些方法对点击、收藏、加入购物车等用户的隐性反馈行为利用得不够,并且没有考虑用户行为偏好的时间敏感性。该文提出了一种基于用户隐性反馈行为的下一个购物篮推荐方法,将用户行为按照一定的时间窗口进行划分,对于每个窗口从多个维度抽取用户对商品的时序偏好特征,运用深度学习领域的卷积神经网络模型进行分类器训练。在真实数据集中的实验结果表明,与传统的线性模型和树模型等分类器相比,该文提出的卷积神经网络框架具有较强的特征萃取能力和泛化能力,提高了推荐系统的用户满意度。

Next Basket Recommendation Based on Implicit User Feedback

“Next Basket” recommendation is a crucial task in E-commerce field. Traditional algorithms can be divided into sequential recommender and general recommender, both of which neglect the impact of implicit feedback behavior and time sensitivity of user's preferences. This paper proposes a “next basket” recommendation framework based on implicit user feedback. We divide the user behaviors into several time windows according to the timestamp of these behaviors, and model the user preference in different dimensions for each window. Then we utilize the convolutional neural network to train a classifier. Compared to traditional linear models and tree models on a real dataset, the proposed model improves the user satisfaction with the recommender system.