TY - GEN
T1 - Spatiooral Dual Graph Attention Network for Query-POI Matching
AU - Yuan, Zixuan
AU - Liu, Hao
AU - Liu, Yanchi
AU - Zhang, Denghui
AU - Yi, Fei
AU - Zhu, Nengjun
AU - Xiong, Hui
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/25
Y1 - 2020/7/25
N2 - 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 spatiooral factors and dynamic user preferences. To this end, in this paper, we develop a spatiooral 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 spatiooral 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 spatiooral 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.
AB - 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 spatiooral factors and dynamic user preferences. To this end, in this paper, we develop a spatiooral 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 spatiooral 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 spatiooral 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.
KW - dual graph neural network
KW - query-poi matching
KW - spatiooral analysis
KW - user modeling
UR - http://www.scopus.com/inward/record.url?scp=85090119584&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090119584&partnerID=8YFLogxK
U2 - 10.1145/3397271.3401159
DO - 10.1145/3397271.3401159
M3 - Conference contribution
AN - SCOPUS:85090119584
T3 - SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 629
EP - 638
BT - SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
T2 - 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020
Y2 - 25 July 2020 through 30 July 2020
ER -