Recommendation systems greatly improve user experience with personalized suggestions. Much research utilizes deep learning to extract user interest features, yet often neglects their real-time and dynamic aspects. This paper proposes a joint learning-based recommendation model (JLS-Rec) for e-commerce platforms, integrating both short-term and long-term user interests. First, for users’ long-term interests, the paper suggests mining more detailed interests from user behavior sequences. This method decouples the behavior sequence in horizontal and vertical directions using a Convolutional Neural Network to learn the user's long-term interests. Second, for users’ short-term interests, the JLS-Rec method learns feature transformations on users’ recent behavior sequences by stacking multiple self-attention mechanisms, resulting in dynamic representations of the user's short-term interests at the current stage. Finally, based on the principle of prioritizing short-term memory without neglecting long-term interests, the paper proposes a joint learning framework with dual embeddings to balance the two characteristics of user long-term and short-term interests. This framework generates accurate recommendation results while utilizing these two interest features to predict user feedback on products. The experimental results demonstrate that the model effectively mines the long-term and short-term interest information of users in the features, thereby improving the recommendation accuracy of e-commerce platforms.

中文翻译：

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融合长短期利益的电商平台联合学习推荐模型


推荐系统通过个性化建议极大地改善了用户体验。许多研究利用深度学习来提取用户兴趣特征，但往往忽略了它们的实时性和动态性。本文提出了一种针对电子商务平台的基于联合学习的推荐模型（JLS-Rec），整合了短期和长期用户兴趣。首先，针对用户的长期兴趣，论文建议从用户行为序列中挖掘更详细的兴趣。该方法利用卷积神经网络解耦水平和垂直方向的行为序列，以学习用户的长期兴趣。其次，针对用户的短期兴趣，JLS-Rec方法通过堆叠多个自注意力机制来学习用户近期行为序列的特征变换，从而得到用户现阶段短期兴趣的动态表示。最后，基于优先考虑短期记忆而不忽视长期兴趣的原则，论文提出了一种具有双嵌入的联合学习框架，以平衡用户长期和短期兴趣两个特征。该框架生成准确的推荐结果，同时利用这两个兴趣特征来预测用户对产品的反馈。实验结果表明，该模型有效挖掘了特征中用户的长期和短期兴趣信息，从而提高了电商平台的推荐准确率。