PDF(Pubmed)
Abstract：
Next Point-of-Interest (POI) recommendation aims to predict the next POI for users from their historical activities. Existing methods typically rely on location-level POI check-in trajectories to explore user sequential transition patterns, which suffer from the severe check-in data sparsity issue. However, taking into account region-level and category-level POI sequences can help address this issue. Moreover, collaborative information between different granularities of POI sequences is not well utilized, which can facilitate mutual enhancement and benefit to augment user preference learning. To address these challenges, we propose multi-granularity contrastive learning (MGCL) for next POI recommendation, which utilizes multi-granularity representation and contrastive learning to improve the next POI recommendation performance. Specifically, location-level POI graph, category-level, and region-level sequences are first constructed. Then, we use graph convolutional networks on POI graph to extract cross-user sequential transition patterns. Furthermore, self-attention networks are used to learn individual user sequential transition patterns for each granularity level. To capture the collaborative signals between multi-granularity, we apply the contrastive learning approach. Finally, we jointly train the recommendation and contrastive learning tasks. Extensive experiments demonstrate that MGCL is more effective than state-of-the-art methods.
摘要：
下一个兴趣点（POI）建议旨在从用户的历史活动中预测用户的下一个POI。现有方法通常依赖于位置级POI签入轨迹来探索用户顺序过渡模式，受到严重的签入数据稀疏性问题的困扰。然而,考虑到区域级别和类别级别的POI序列可以帮助解决这个问题。此外，不同粒度的POI序列之间的协作信息没有得到很好的利用，这可以促进相互增强，有利于增强用户偏好学习。为了应对这些挑战，我们提出了多粒度对比学习(MGCL)用于下一个POI推荐，它利用多粒度表示和对比学习来提高下一个POI推荐性能。具体来说,位置级POI图，类别级别,首先构建区域水平的序列。然后，我们在POI图上使用图卷积网络来提取跨用户的顺序过渡模式。此外，自我注意网络用于学习每个粒度级别的单个用户顺序过渡模式。为了捕获多粒度之间的协作信号，我们采用对比学习方法。最后,我们共同训练推荐和对比学习任务。大量实验证明MGCL比现有技术方法更有效。
