Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

Sun, Kaiqiong; Qian, Tieyun; Chen, Tong; Liang, Yile; Nguyen, Quoc Viet Hung; Yin, Hongzhi

doi:10.1609/aaai.v34i01.5353

Cited by 252 publications

(133 citation statements)

References 19 publications

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“…It leverages category information to construct a knowledge graph for next POI recommendation. • LSTPM [26] is a state-of-the-art LSTM-based method, which captures long-term and short-term preferences with a nonlocal network and a geo-dilated RNN respectively.…”

Section: Baselinesmentioning

confidence: 99%

“…Spatio-Temporal Gated Network (STGN) [39] and Hierarchical Spatial-Temporal LSTM (HST-LSTM) [15] explicitly leverage physical distances between POIs as model inputs. Ke et al [26] proposes a geo-dilated LSTM to exploit geographical inluences among non-successive POIs by constructing a novel distance-based input set. The second type of methods assumes that POIs within the same check-in sequences possessing great inluence on each other, implicitly modeling these co-occurring relations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation

Wang

Zhu

Zhang

et al. 2022

ACM Trans. Knowl. Discov. Data

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The task of next Point-of-Interest (POI) recommendation aims to recommend a list of POIs for a user to visit at the next timestamp based on his/her previous interactions, which is valuable for both location-based service providers and users. Recent state-of-the-art studies mainly employ recurrent neural network (RNN) based methods to model user check-in behaviors according to user’s historical check-in sequences. However, most of the existing RNN-based methods merely capture geographical influences depending on physical distance or successive relation among POIs. They are insufficient to capture the high-order complex geographical influences among POI networks, which are essential for estimating user preferences. To address this limitation, we propose a novel Graph-based Spatial Dependency modeling (GSD) module, which focuses on explicitly modeling complex geographical influences by leveraging graph embedding. GSD captures two types of geographical influences, i.e., distance-based and transition-based influences from designed POI semantic graphs. Additionally, we propose a novel Graph-enhanced Spatial-Temporal network (GSTN) which incorporates user spatial and temporal dependencies for next POI recommendation. Specifically, GSTN consists of a Long Short-Term Memory (LSTM) network for user-specific temporal dependencies modeling and GSD for user spatial dependencies learning. Finally, we evaluate the proposed model using three real-world datasets. Extensive experiments demonstrate the effectiveness of GSD in capturing various geographical influences and the improvement of GSTN over state-of-the-art methods.

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Section: Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation

Wang

Zhu

Zhang

et al. 2022

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

show abstract

“…This is because the user's own range of activities is limited, and the predicted POI should not deviate too much from the user's regular range of activities. Otherwise, the recommendation will become meaningless [34]. The specific loss function is as follows:…”

Section: Loss Function and Optimization Algorithmmentioning

confidence: 99%

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Wang

Lü

2021

Complex Intell. Syst.

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Aiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

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“…However, existing RNN methods neglect some details of users preferences, thus making the recommendation results unreliable. To address the above limitations, Sun et al [43] proposed a method named LSTPM for next POI recommendation. Li et al [44] introduce a novel neural network model named TMCA that employed the LSTM-based encoder-decoder framework for the next POI recommendation.…”

Section: Sequential Poi Recommendationmentioning

confidence: 99%

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

2021

Wireless Communications and Mobile Computing

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The POI recommendation system has become an important means to help people discover attractive and interesting places. Based on our data analysis, we observe that users pay equal attention to conservatism and curiosity. In particular, adopting analysis corresponding to different time intervals, we find that users lean towards old POIs in the short term and look for new POIs with the increase of the time interval. However, existing approaches usually neglect users’ conservatism and curiosity preferences. Therefore, they are confronted with a bottleneck of depicting accurate user needs, making it difficult to improve the recommendation performance further. Besides, we further find that the number of user daily check-ins has uneven distribution, which is not conducive to capture the accurate transition patterns of user behaviors. In light of the above, we design a single POI sequential method. On this basis, we propose a recommendation method of the variable additive Markov chain. We consider the user sequential preferences, especially liking old and pursuing new features. In addition, our model exploits the geographical tendency of user behaviors. Finally, we conduct abundant experiments on four cities in the two real datasets, i.e., Foursquare and Jiepang. The experimental results show its superiority over other competitors.

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Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

Cited by 252 publications

References 19 publications

Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation

Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

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