基于自监督学习的社交网络用户轨迹预测模型

针对当前用户轨迹数据建模中存在的签到点稀疏性、长时间依赖性和移动模式复杂等问题，提出基于自监督学习的社交网络用户轨迹预测模型SeNext，对用户轨迹进行建模和训练来预测用户的下一个兴趣点（POI）。首先，使用数据增强的方式来丰富训练数据样本，以解决数据不足及个别用户足迹太少导致的模型泛化能力不足的问题；其次，将循环神经网络（RNN）、卷积神经网络（CNN）和注意力机制分别用于当前轨迹和历史轨迹的建模中，以此从高维稀疏的数据中提取有用的表示，用来匹配用户过去最相似的移动方式。最后，通过结合自监督学习并引入对比损失优化噪声对比估计（InfoNCE），SeNext在潜在空间学习隐含表示来预测用户的下一个POI。实验结果表明，在纽约数据集上，SeNext比最新的VANext（Variational Attention based Next）模型的预测准确度在Top@1上提高了11.10%左右。

Trajectory prediction model of social network users based on self-supervised learning

Aiming at the existing problems in user trajectory data modeling such as the sparsity of check-in points, long-term dependencies and complex moving patterns, a social network user trajectory prediction model based on self-supervised learning, called SeNext, was proposed to model and train the user trajectory to predict the next Point Of Interest (POI) of the user. First, data augmentation was utilized to expand the training trajectory samples, which solved the problem of the deficiency of model generalization capability caused by insufficient data and too few footprints of some users. Second, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and attention mechanism were adopted into the modeling of current and historical trajectories respectively, so as to extract effective representations from high-dimensional sparse data to match the most similar moving patterns of users in the past. Finally, SeNext learned the implicit representations in the latent space by combining self-supervised learning and introducing contrastive loss Noise Contrastive Estimation (InfoNCE) to predict the next POI of the user. Experimental results show that compared to the state-of-the-artVariational Attention based Next (VANext)model, SeNext improves the prediction accuracy about 11% on Top@1.