Contrastive Trajectory Learning for Tour Recommendation
Contrastive Trajectory Learning for Tour Recommendation
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DOI：
10.1145/3462331
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发表时间：
2021-11
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通讯作者：
Fan Zhou;Pengyu Wang;Xovee Xu;Wenxin Tai;Goce Trajcevski
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作者：
Fan Zhou;Pengyu Wang;Xovee Xu;Wenxin Tai;Goce Trajcevski
个性化旅游推荐（PTR）的主要目标是根据用户特定的约束，如持续时间、起点和终点、计划参观的景点数量等，为特定的游客生成一系列兴趣点（poi）。以前的PTR解决方案要么是基于启发式方法来解决定向问题，以在指定的预算下最大化全局奖励，要么是试图通过随机过程或循环神经网络来学习用户访问偏好和转换模式的方法。然而，现有的学习方法依赖于历史行程来训练模型，并使用下一个访问的POI作为监督信号，这可能无法完全捕获偏好的一致性，从而向不同的用户推荐相似的行程，这主要是由于数据稀疏性问题和POI流行度的长尾分布。本文提出了一种新颖的旅游推荐模型，通过自监督的方式从旅行中提取知识和监督信号。本文提出了旅行推荐的对比轨迹学习方法（CTLTR），该方法利用固有的POI依赖关系和旅行意图来发现额外的知识，并通过预训练辅助自监督目标来增强稀疏数据。CTLTR提供了一种原则性的方法来描述固有的数据相关性，同时通过学习适用于旅行规划的鲁棒表示来解决隐式反馈和弱监督问题。我们引入了一种分层循环编码器来识别游客的意图，并利用对比损失来发现子序列语义及其序列模式，通过最大化互信息。此外，我们观察到数据增强步骤作为对比学习的初步步骤可以解决由数据稀疏性引起的过拟合问题。我们在一系列真实世界的数据集上进行了广泛的实验，并证明我们的模型可以在推荐准确性和访问订单方面显著提高推荐性能。
The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.