Zhang Xiangde, Zhou Yuan, Wang Jianping, Lu Xiaojun
College of Sciences, Northeastern University, Shenyang 110819, China.
Entropy (Basel). 2021 Nov 12;23(11):1500. doi: 10.3390/e23111500.
Session-based recommendations aim to predict a user's next click based on the user's current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user's interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users' interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users' interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users' long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.
基于会话的推荐旨在根据用户当前和历史会话预测用户的下一次点击，这可应用于购物网站和应用程序。现有的基于会话的推荐方法无法准确捕捉项目之间的复杂转换。此外，一些方法将会话压缩成固定的表示向量，而不考虑用户当前的兴趣偏好，从而限制了推荐的准确性。考虑到项目和用户兴趣的多样性，提出了一种用于基于会话推荐的个性化兴趣注意力图神经网络（PIA-GNN）。该方法利用个性化图卷积网络（PGNN）捕捉项目之间的复杂转换，调用兴趣感知机制来自适应地激活用户对不同项目的兴趣。此外，在捕捉用户长期偏好时，使用自注意力层来捕捉项目之间的长期依赖关系。在本文中，使用交叉熵损失作为目标函数来训练我们的模型。我们在两个真实数据集上进行了丰富的实验，结果表明PIA-GNN优于现有的个性化会话感知推荐方法。
