Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction

Cui, Qiang; Tang, Yuyuan; Wu, Shu; Wang, Liang

doi:10.1007/978-3-030-16142-2_23

Cited by 22 publications

(19 citation statements)

References 15 publications

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“…Zhao et al [60] proposed a new variant of LSTM, which implemented time gates and distance gates into LSTM to capture the spatiotemporal relation between successive check-ins. Cui et al [22] proposed a Distance-to-Preference (Distance2Pre) network for the next POI prediction, which modeled check-in sequences and successive distances to acquire the user's sequential preference and spatial preference. However, the above methods were unable to capture different contributions of each POIs in the history check-in sequence.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

“…After pre-processing, the basic statistics of two datasets are summarized in Table 2. Inspired by previous studies [22,26], we employed the leave-one-out evaluation. We used the last POI of each user's check-in sequence as the test data and the remaining POI as the training data.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…In this subsection, we train our ATST-GRU network under the Bayesian Personalized Ranking (BPR) framework by using the backpropagation through time (BPTT) algorithm. These methods have been successfully used for network training of RNN based recommendation models [8,22]. BPR is a pairwise ranking framework that is widely used to process implicit feedback data.…”

Section: Network Learningmentioning

confidence: 99%

“…Recently, recurrent neural network (RNN) [17] and its variants (e.g., long-short term memory (LSTM) [18] and gated recurrent unit (GRU) [19,20]) have been successfully applied to sequential recommender systems [21]. The hidden states of RNN methods have both characteristics in nature and are adequate for modeling sequential correlations and temporal dynamics in POI recommender systems [8,22], and they can better capture the long-term dependency. However, the existing RNN based POI recommendation methods have difficulty in alleviating the cold-start problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Liu

Wang

et al. 2019

IJGI

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Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user's preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user's main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user's sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user's main behavioral intention in the user's check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

Section: Baseline Methodsmentioning

confidence: 99%

Section: Network Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Liu

Wang

et al. 2019

IJGI

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“…As per Tobler's First Law of Geography [11] that "Everything is related to everything else, but near things are more related than distant things", adjacent POIs are more geographically relevant than distant POIs. In the literature, spatial influence has been mostly modeled by utilizing the distance between two POIs; moreover, many existing studies have shown that there is a strong relationship between user's check-in activities and geographical distances [12,13]. Besides, temporal context and sequential relations are also crucial factors that affect human real-life check-in activities [7,[14][15][16] due to the time sensitivity of the POI recommendation.…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation

Liu

et al. 2020

IJGI

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With the growing popularity of location-based social media applications, point-of-interest (POI) recommendation has become important in recent years. Several techniques, especially the collaborative filtering (CF), Markov chain (MC), and recurrent neural network (RNN) based methods, have been recently proposed for the POI recommendation service. However, CF-based methods and MC-based methods are ineffective to represent complicated interaction relations in the historical check-in sequences. Although recurrent neural networks (RNNs) and its variants have been successfully employed in POI recommendation, they depend on a hidden state of the entire past that cannot fully utilize parallel computation within a check-in sequence. To address these above limitations, we propose a spatiotemporal dilated convolutional generative network (ST-DCGN) for POI recommendation in this study. Firstly, inspired by the Google DeepMind’ WaveNet model, we introduce a simple but very effective dilated convolutional generative network as a solution to POI recommendation, which can efficiently model the user’s complicated short- and long-range check-in sequence by using a stack of dilated causal convolution layers and residual block structure. Then, we propose to acquire user’s spatial preference by modeling continuous geographical distances, and to capture user’s temporal preference by considering two types of time periodic patterns (i.e., hours in a day and days in a week). Moreover, we conducted an extensive performance evaluation using two large-scale real-world datasets, namely Foursquare and Instagram. Experimental results show that the proposed ST-DCGN model is well-suited for POI recommendation problems and can effectively learn dependencies in and between the check-in sequences. The proposed model attains state-of-the-art accuracy with less training time in the POI recommendation task.

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Attraction Recommendation Based on Tourism Context Modeling and Multi‐neural Collaborative Filtering Algorithm

Zhang

Wang

Fei

et al. 2023

IEEJ Transactions Elec Engng

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The key to generating tailored suggestions in the scenic location recommendation scene is how to model the diverse tourism environments in order to correctly acquire visitor preferences and scenic spot tourism features. However, most existing recommendation algorithms focus on the spatiotemporal background modeling of historical tourism trajectories. Only tourists' preferences are understood, and the rich heterogeneous tourism information such as tourists' personal tourism constraints and scenic spots' tourism attributes are ignored. In this paper, we proposed a multiple neural collaborative filtering attraction recommendation architecture (MNCF-AR). To begin, we learn the tourism feature representation of tourists by modeling diverse tourism contexts, and then we create the tourism trajectory background of tourists using a large number of actual tourism logs to achieve the entire feature representation of tourists. Second, to learn the feature vector of scenic spots in the context, the tourism heterogeneous network map is used to build the scenic spot attribute background, the self-attention network to learn the scenic spot sequence learning, and a neural network to project each scenic spot into a unified potential feature space. Finally, the multi-neural collaborative filtering method is utilized to forecast the difference in scores between visitors and scenic sites so that tailored scenic spots may be recommended. Extensive experiments on mainstream datasets, such as MaFengwo, New York, Tokyo and Xi'an, show that the proposed method can effectively and accurately recommend attractions for users.

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Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction

Cited by 22 publications

References 15 publications

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest Recommendation

Attraction Recommendation Based on Tourism Context Modeling and Multi‐neural Collaborative Filtering Algorithm

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