Attentive multi-task learning for group itinerary recommendation
Attentive multi-task learning for group itinerary recommendation
复制标题
专注多任务学习团体行程推荐
DOI：
10.1007/s10115-021-01567-3
复制
发表时间：
2021-04-27
影响因子：
2.7
通讯作者：
Zhu, Guixiang
中科院分区：
文献类型：
--
作者：
Chen, Lei;Cao, Jie;Zhu, Guixiang
旅游业是最大的服务业之一，也是人们与朋友或家人一起参与的一项受欢迎的休闲活动。旅游者面临的一个重要问题是如何规划兴趣点序列，以保持群体偏好和给定的时间和空间约束之间的平衡。传统的团队行程推荐方法大多采用预定义的偏好聚合策略，没有考虑团队成员的显著特征和内在联系。此外，POI文本信息有利于捕捉整体群体偏好，但很少被考虑。针对这些问题，本文提出了一个基于注意力多任务学习的群体行程推荐（AMT-IRE）框架，该框架能够动态学习群体成员之间的内在关系，并通过注意力机制获得一致的群体偏好。同时，AMT-IRE通过另一个注意力网络集成POI类别和POI文本信息。最后，群体偏好被用于定向越野问题的一个变体中，以推荐团体行程。在六个数据集上的实验验证了AMT-IRE的有效性。
Tourism is one of the largest service industries and a popular leisure activity participated by people with friends or family. A significant problem faced by the tourists is how to plan sequences of points of interest (POIs) that maintain a balance between the group preferences and the given temporal and spatial constraints. Most traditional group itinerary recommendation methods adopt predefined preference aggregate strategies without considering the group members’ distinctive characteristics and inner relations. Besides, POI textual information is beneficial to capture overall group preferences but is rarely considered. With these concerns in mind, this paper proposes an AMT-IRE (short for Attentive Multi-Task learning-based group Itinerary REcommendation) framework, which can dynamically learn the inner relations between group members and obtain consensus group preferences via the attention mechanism. Meanwhile, AMT-IRE integrates POI categories and POI textual information via another attention network. Finally, the group preferences are used in a variant of the orienteering problem to recommend group itineraries. Extensive experiments on six datasets validate the effectiveness of AMT-IRE.