Spatio-Temporal Dual Graph Attention Network for Query-POI Matching
Spatio-Temporal Dual Graph Attention Network for Query-POI Matching
复制标题
用于查询 POI 匹配的时空双图注意力网络
DOI：
10.1145/3397271.3401159
复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Xiong, hui
中科院分区：
文献类型：
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作者：
Yuan, Zixuan;Liu, Hao;Zhang, Denghui;Yi, Fei;Zhu, Nengjun;Xiong, hui
在导航和叫车等基于位置的服务中，将查询与兴趣点 (POI) 进行匹配以实现高效的目的地检索是一项重要功能。事实上，由于空间限制和实时性要求，此类服务通常需要在仅输入部分搜索关键字时得到中间的POI匹配结果。虽然有许多用于一般文本语义匹配的检索模型，但很少有人尝试考虑丰富的时空因素和动态用户偏好的集成来进行查询 POI 匹配。为此，在本文中，我们开发了一种时空双图注意网络~（STDGAT），它可以联合建模动态情境上下文和用户的顺序行为，以进行智能查询-POI 匹配。具体来说，我们首先利用语义表示块对不完整文本之间的语义相关性以及通过位置和时间捕获的各种时空因素进行建模。接下来，我们提出了一种新颖的双图注意力网络来捕获两种类型的查询 - POI 相关性，其中一种模型全局查询 - POI 交互，另一种模型模型随时间演变的用户对目的地 POI 的偏好。此外，我们还将时空因素纳入双图注意网络中，以便查询 POI 相关性可以推广到复杂的情境上下文。之后，引入成对融合策略来提取查询和 POI 的显着全局特征代表。最后，提出了几种冷启动策略和训练方法，以提高匹配效果和训练效率。对两个现实世界数据集的广泛实验证明了我们的方法与最先进的基线相比的性能。结果表明，即使只给出部分查询关键词，我们的模型在匹配精度方面也取得了显着的提高。
In location-based services, such as navigation and ride-hailing, it is an essential function to match a query with Point-of-Interests (POIs) for efficient destination retrieval. Indeed, due to the space limit and real-time requirement, such services usually require intermediate POI matching results when only partial search keywords are typed. While there are numerous retrieval models for general textual semantic matching, few attempts have been made for query-POI matching by considering the integration of rich spatio-temporal factors and dynamic user preferences. To this end, in this paper, we develop a spatio-temporal dual graph attention network ~(STDGAT), which can jointly model dynamic situational context and users' sequential behaviors for intelligent query-POI matching. Specifically, we first utilize a semantic representation block to model semantic correlations among incomplete texts as well as various spatio-temporal factors captured by location and time. Next, we propose a novel dual graph attention network to capture two types of query-POI relevance, where one models global query-POI interaction and another one models time-evolving user preferences on destination POIs. Moreover, we also incorporate spatio-temporal factors into the dual graph attention network so that the query-POI relevance can be generalized to the sophisticated situational context. After that, a pairwise fusion strategy is introduced to extract the salient global feature representatives for both queries and POIs. Finally, several cold-start strategies and training methods are proposed to improve the matching effectiveness and training efficiency. Extensive experiments on two real-world datasets demonstrate the performances of our approach compared with state-of-the-art baselines. The results show that our model achieves significant improvement in terms of matching accuracy even with only partial query keywords are given.