Personalized POI Recommendation: Spatio-Temporal Representation Learning with Social Tie

Dai, Shaojie; Yu, Yanwei; Fan, Hao; Dong, Junyu

doi:10.1007/978-3-030-73194-6_37

Cited by 8 publications

(8 citation statements)

References 28 publications

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“…In addition, PLSPL [25] adopts the attention mechanism to model the long-term preference and employs two LSTM models to model the short-term preference on location-based and category-based sequence, respectively. Nevertheless, PLSPL does not utilize the geographical information and social ties, which play important roles in POI recommendation task [5,6,32].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This work is extended from a conference paper [6]. The differences between this work and the conference paper are summarized as follows: First, we extend PPR [6] to an endto-end POI recommendation model, named GCN-LSTM. GCN-LSTM jointly learns user and POI representations using Graph Convolutional Network (GCN) in the constructed heterogeneous graph and captures the user personalized sequential preference using the spatio-temporal neural network.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Representation Learning with Social Tie for Personalized POI Recommendation

Dai

Fan

et al. 2022

Data Sci. Eng.

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Recommending a limited number of Point-of-Interests (POIs) a user will visit next has become increasingly important to both users and POI holders for Location-Based Social Networks (LBSNs). However, POI recommendation is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recent studies show that embedding techniques effectively incorporate POI contextual information to alleviate the data sparsity issue, and Recurrent Neural Network (RNN) has been successfully employed for sequential prediction. Nevertheless, existing POI recommendation approaches are still limited in capturing user personalized preference due to separate embedding learning or network modeling. To this end, we propose a novel unified spatio-temporal neural network framework, named PPR, which leverages users’ check-in records and social ties to recommend personalized POIs for querying users by joint embedding and sequential modeling. Specifically, PPR first learns user and POI representations by joint modeling User-POI relation, sequential patterns, geographical influence, and social ties in a heterogeneous graph and then models user personalized sequential patterns using the designed spatio-temporal neural network based on LSTM model for the personalized POI recommendation. Furthermore, we extend PPR to an end-to-end recommendation model by jointly learning node representations and modeling user personalized sequential preference. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms state-of-the-art baselines for successive POI recommendation in terms of Accuracy, Precision, Recall and NDCG. The source code is available at: https://www.anonymous.4open.science/r/DSE-1BEC.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Representation Learning with Social Tie for Personalized POI Recommendation

Dai

Fan

et al. 2022

Data Sci. Eng.

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“…Recently, Wu et al design a graph neural network based POI recommendation model that comprehensively utilizes collaborative filtering information and spatiotemporal information [15]. The PPR [6] model integrates graph embedding technology and spatiotemporal neural network to improve the accuracy of top-K POI recommendations. Inspired by previous research, we comprehensively consider the contextual information, influencing factors of user and POI and combine the attention mechanism to recommend a list of POIs for each user that he/she is interested but never visited.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, GE [5] verifies that using graph embedding technology and unified joint training of multiple factors can make more accurate location recommendation. PPR [6] model improve the accuracy of prediction by jointly embedding user and POI information and using spatiotemporal neural network framework. The research of GE and PPR shows that graph embedding technology has a good application effect in POI recommendation.…”

Section: Introductionmentioning

confidence: 99%

Multi-view Attention Mechanism Learning for POI Recommendation

Zhu

Dai

2022

J. Phys.: Conf. Ser.

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POI(point of interest) recommendation is a very necessary research field in both academic and commerce, however, predicting users’ potential points of interest is always faced with the problems of data sparsity and context semantics. Some studies have shown that graph embedding technology alleviates the problem of data sparsity to a certain extent. However, neither graph embedding techniques nor unsupervised learning models can adaptively learn the different effects of multiple relations between users and POIs, respectively. In view of this, we leverage the contextual information of users and POIs to build the multi-view affinity graphs(e.g. User-User, POI-POI and User-POI), and learn the latent representations of users and POIs based on the Graph Embedding technology and Attention mechanism, namely the GEA model. In particular, we first construct multi-view affinity graphs by using user’s social relationship, geographical distance and check-in behaviour, and embed them into a low dimensional shared space to learn the latent representation of users and POIs. Afterwards, in order to take advantage of the different effects of multiple relationships in the final recommendation task, we exploit the attention mechanism to obtain the fused latent representation and make recommendation according users’ potential preferences. Finally, we design a multi-task objective function for joint optimization to obtain more accurate recommendation results. Extensive experiments on Gowalla have verified the effectiveness of our model.

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PreCLN: Pretrained-based contrastive learning network for vehicle trajectory prediction

et al. 2022

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Trajectory prediction of vehicles is of great importance to various smart city applications ranging from transportation scheduling, vehicle navigation, to location-based advertisements. Existing methods all focus on modeling spatiotemporal relations with explicit contextual semantics from labeled trajectory data, and rarely consider the effective use of large amounts of available unlabeled trajectory data with the assistance of contrastive learning and pre-training techniques. To this end, we develop a novel Pretrained-based Contrastive Learning Network (PreCLN) for vehicle trajectory prediction. Specifically, we propose a dual-view trajectory contrastive learning framework to achieve self-supervised pre-training. A Transformer-based trajectory encoder is designed to effectively capture the long-term spatiotemporal dependencies in trajectories to embed input trajectories into fixedlength representation vectors. Moreover, three auxiliary pre-training tasks, i.e., trajectory imputation, trajectory destination prediction, and trajectory-user linking, are used to assist the training of Pre-CLN with the dual-view trajectory contrastive learning framework.

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Personalized POI Recommendation: Spatio-Temporal Representation Learning with Social Tie

Cited by 8 publications

References 28 publications

Spatio-Temporal Representation Learning with Social Tie for Personalized POI Recommendation

Spatio-Temporal Representation Learning with Social Tie for Personalized POI Recommendation

Multi-view Attention Mechanism Learning for POI Recommendation

PreCLN: Pretrained-based contrastive learning network for vehicle trajectory prediction

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