Personalized POI Recommendation Model in LBSNs

2017 ◽  
pp. 608-613
Author(s):  
Zhong Guo ◽  
Ma Changyi
Keyword(s):  
Poi Recommendation

An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Liwei Huang ◽  
Yutao Ma ◽  
Shibo Wang ◽  
Yanbo Liu
Keyword(s):  
Poi Recommendation ◽  
Lstm Network

Attentional Memory Network with Correlation-based Embedding for time-aware POI recommendation

2021 ◽  
pp. 106747
Author(s):  
Meihui Shi ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie ◽  
Ge Yu
Keyword(s):  
Poi Recommendation ◽  
Memory Network ◽  
Time Aware

An attention‐based category‐aware GRU model for the next POI recommendation

2021 ◽  
Author(s):  
Yuwen Liu ◽  
Aixiang Pei ◽  
Fan Wang ◽  
Yihong Yang ◽  
Xuyun Zhang ◽  
...  
Keyword(s):  
Poi Recommendation

Real-time event embedding for POI recommendation

2019 ◽  
Vol 349 ◽  
pp. 1-11 ◽  
Author(s):  
Pei-Yi Hao ◽  
Weng-Hang Cheang ◽  
Jung-Hsien Chiang
Keyword(s):  
Real Time ◽  
Poi Recommendation

Online meta-learning for POI recommendation

2022 ◽  
Author(s):  
Yao Lv ◽  
Yu Sang ◽  
Chong Tai ◽  
Wanjun Cheng ◽  
Jedi S. Shang ◽  
...  
Keyword(s):  
Poi Recommendation ◽  
Meta Learning

scholarly journals Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification

2019 ◽  
Vol 33 ◽  
pp. 5458-5465 ◽  
Author(s):  
Dongbo Xi ◽  
Fuzhen Zhuang ◽  
Yanchi Liu ◽  
Jingjing Gu ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Location Prediction ◽  
Temporal Dependence ◽  
Great Opportunity ◽  
Mobility Data ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Spatio Temporal ◽  
Dynamic Preferences

Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e.g., geolocation information missing, unreal check-ins, data sparsity) in real-life mobility data limit the effectiveness of existing POIoriented studies, e.g., POI recommendation and location prediction, when applied to real applications. To this end, in this paper, we develop a model, named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence and users’ dynamic preferences, to identify the missing POI check-in where a user has visited at a specific time. Specifically, we first utilize bi-directional global spatial and local temporal information of POIs to capture the complex dependence relationships. Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users’ dynamic preferences. Moreover, the dynamic preferences are transformed into the same space as the dependence relationships to form the final model. Finally, the proposed model is evaluated on three large-scale real-world datasets and the results demonstrate significant improvements of our model compared with state-of-the-art methods. Also, it is worth noting that the proposed model can be naturally extended to address POI recommendation and location prediction tasks with competitive performances.

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