An Attention-Based Spatiotemporal LSTM Network for Next POI Recommendation

Huang, Liwei; Ma, Yutao; Wang, Shibo; Yan-bo, Liu

doi:10.1109/tsc.2019.2918310

Cited by 147 publications

(92 citation statements)

References 26 publications

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“…Moreover, some researchers have integrated attention models into RNNs and achieved better performance. For example, Huang et al [10] developed an attention-based spatiotemporal LSTM (ATST-LSTM) network for the next POI recommendation, which considered the relevant historical check-in records in a check-in sequence selectively using the spatiotemporal contextual information. Feng et al [43] proposed an attentional mobility model, namely DeepMove, which predicted human mobility from lengthy and sparse trajectories.…”

Section: Deep Learning-based Poi Recommendation Methodsmentioning

confidence: 99%

“…In this part, we model check-in sequences and capture personalized spatiotemporal preference by considering geographical influences and temporal periodic patterns. Recent studies show that continuous geographic movement and temporal periodic patterns are important for POI recommendations [10,13,16,19].…”

Section: Personalized Spatiotemporal Preferencementioning

confidence: 99%

“…Therefore, the research on POI recommendation has attracted widespread attention from the academic and industrial fields [6][7][8][9]. Unlike items such as news, videos, and music in traditional context-free recommender systems, the user's history check-in data implies the interactions between a user and POIs in a physical world [10]. Thus, geospatial information, such as geographical distance, would have a significant effect on user's daily activities and check-in behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…However, traditional userbased CF methods, item-based CF methods, and matrix factorization (MF)-based CF methods find it difficult to handle long-range sequences and incorporate various features effectively because they only learn linear or low-order interactions between features. Moreover, MC-based methods assume Unlike items such as news, videos, and music in traditional context-free recommender systems, the user's history check-in data implies the interactions between a user and POIs in a physical world [10]. Thus, geospatial information, such as geographical distance, would have a significant effect on user's daily activities and check-in behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, MC-based methods assume strong independence among different components and only utilize the last POI when modeling check-in sequences. Recently, deep learning-based methods, especially RNNs-based methods, have been applied in POI recommendation and were assumed to be effective [10,13,19]. RNNs-based methods outperform other POI recommendation methods since they can learn long-range dependencies effectively.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Deep Learning-based Poi Recommendation Methodsmentioning

confidence: 99%

Section: Personalized Spatiotemporal Preferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liu

et al. 2020

IJGI

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DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Safavi

Jalali

2022

Concurrency and Computation

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Today, millions of active users spend a percentage of their time on location-based social networks like Yelp and Gowalla and share their rich information. They can easily learn about their friends' behaviors and where they are visiting and be influenced by their style. As a result, the existence of personalized recommendations and the investigation of meaningful features of users and Point of Interests (POIs), given the challenges of rich contents and data sparsity, is a substantial task to accurately recommend the POIs and interests of users in location-based social networks (LBSNs). This work proposes a novel pipeline of POI recommendations named DeePOF based on deep learning and the convolutional neural network. This approach only takes into consideration the influence of the most similar pattern of friendship instead of the friendship of all users. The mean-shift clustering technique is used to detect similarity. The most similar friends' spatial and temporal features are fed into our deep CNN technique. The output of several proposed layers can predict latitude and longitude and the ID of subsequent appropriate places, and then using the friendship interval of a similar pattern, the lowest distance venues are chosen. This combination method is estimated on two popular datasets of LBSNs. Experimental results demonstrate that analyzing similar friendships could make recommendations more accurate and the suggested model for recommending a sequence of top-k POIs outperforms state-of-the-art approaches.

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

et al. 2021

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With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior.Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network.Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based categoryaware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.

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An Attention-Based Spatiotemporal LSTM Network for Next POI Recommendation

Cited by 147 publications

References 26 publications

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

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

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

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

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