POI Recommendation Method Using Deep Learning in Location‐Based Social Networks

Liu, Yang; Wu, An-bo

doi:10.1155/2021/9120864

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…It modeled user-POI, user-category, POI-time, and POI-user dependencies to mine user preferences. Liu and Wu [23] deployed a Bi-LSTM attention mechanism to analyze long-and short-term preferences. In conjunction with the Bi-LSTM, the encoder and decoder sequence further analyze the POI sequence data to recommend the top-N POIs.…”

Section: Related Workmentioning

confidence: 99%

“…6) SDAE-Bi-LSTM [39]: A combination of stacked autoencoders (SDAE) and Bi-LSTM 7) DeepPOF [18]: It exploits the social links in addition to spatial and temporal content with a deep neural network. 8) RecPOID [40]: It can be defined as a friendship-aware spatial-temporal context-mining approach 9) Bi-LSTM + Attention [41]: Spatial-temporal context processed through encoder-decoder-based Bi-LSTM model. 10) GT-HAN [42]: This framework models geographicaltemporal aspects using an attention network and caters to POI-POI dependencies.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest Recommendation

Acharya,

Mohbey,

Rajput

2024

IEEE Access

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The growth of the tourism industry has greatly boosted the Point-of-Interest (POI) recommendation tasks using Location-based Social Networks (LBSNs). The ever-evolving nature of user preferences poses a major problem. To address this, we propose a Long-term Preference Mining (LTPM) approach that utilizes the temporal recency (TR) measure in the visits along with the location-aware recommendation based on spatial proximity (SP) to the user's location. The temporal dynamics and changing preferences are exploited based on the modified Long Short-term Memory (LSTM) that utilizes the time decay. The spatial considerations are modeled in two aspects: geographical proximity based on enhanced representation learning using orthogonal mapping. Second, the Region-of-Interest (ROI) is based on spatial griding and metric learning to capture the spatial relationships between POIs to enhance the metric space representation. The final recommendations are based on a multi-head attention mechanism that allocates the weights to different features. The combination of three models, called, LTPM-TRSP approach captures the user-POI, POI-POI, and POI-time relationships by focusing on the informative representation of sequential and spatial data. The category-aware final recommendations based on comprehensive historical behavior and geographical context are quite efficacious. The experimentation on three real-world datasets, Gowalla, Foursquare, and Weeplaces, also suggests the potency compared to other state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest Recommendation

Acharya,

Mohbey,

Rajput

2024

IEEE Access

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“…Meanwhile, Transformer can realize parallel operations, obtain global information, and effectively solve the above problems. Therefore, researchers applied self-attention mechanism to sequential recommendation and achieved good results [23,24]. For example, Liu et al [6] proposed a category-aware GRU model based on the self-attention mechanism, which can selectively use the self-attention mechanism to focus on the relevant historical check-in trajectory information and then conduct POI recommendation.…”

Section: Sequential Next Poi Recommendationmentioning

confidence: 99%

Spatio-Temporal Unequal Interval Correlation-Aware Self-Attention Network for Next POI Recommendation

Huang

et al. 2022

IJGI

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As the core of location-based social networks (LBSNs), the main task of next point-of-interest (POI) recommendation is to predict the next possible POI through the context information from users’ historical check-in trajectories. It is well known that spatial–temporal contextual information plays an important role in analyzing users check-in behaviors. Moreover, the information between POIs provides a non-trivial correlation for modeling users visiting preferences. Unfortunately, the impact of such correlation information and the spatio–temporal unequal interval information between POIs on user selection of next POI, is rarely considered. Therefore, we propose a spatio-temporal unequal interval correlation-aware self-attention network (STUIC-SAN) model for next POI recommendation. Specifically, we first use the linear regression method to obtain the spatio-temporal unequal interval correlation between any two POIs from users’ check-in sequences. Sequentially, we design a spatio-temporal unequal interval correlation-aware self-attention mechanism, which is able to comprehensively capture users’ personalized spatio-temporal unequal interval correlation preferences by incorporating multiple factors, including POIs information, spatio-temporal unequal interval correlation information between POIs, and the absolute positional information of corresponding POIs. On this basis, we perform next POI recommendation. Finally, we conduct comprehensive performance evaluation using large-scale real-world datasets from two popular location-based social networks, namely, Foursquare and Gowalla. Experimental results on two datasets indicate that the proposed STUIC-SAN outperformed the state-of-the-art next POI recommendation approaches regarding two commonly used evaluation metrics.

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“…With the rapid development of LBSN services, users can check in real-world POI through mobile devices and share such check-in with friends to generate richer space, time, social, and content information to improve personalized POI recommendations (Hao et al, 2019;Wand et al, 2017). The deep learning method uses a large amount of data to train the model and mine the potential information from the data (Liu & Wu, 2021). Therefore, deep learning can better extract features and learn the high-order interaction between features.…”

Section: Introductionmentioning

confidence: 99%

POI Recommendation Model Using Multi-Head Attention in Location-Based Social Network Big Data

Liu

2023

International Journal of Information Technologies and Systems Approach

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A point of interest (POI) recommendation model using deep learning in location-based social network big data is proposed. Firstly, the features of POI are divided into inherent features composed of attributes such as geographical location and category, and semantic features of relevance composed of spontaneous access by users. Secondly, the inherent attribute features and semantic features of POI are extracted by constraint matrix decomposition and word vector model respectively, and the two hidden vectors are spliced into the feature vectors of POI to solve the problems of data sparsity and cold start. Finally, the multi-head attention is used to obtain the key information of user preferences, and a deep learning recommendation framework is constructed to model the nonlinear interaction between features. Experiments show that when the recommendation list is 10, the precision and recall of the proposed method are 0.118 and 0.135 respectively, which are better than the comparative recommendation method.

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POI Recommendation Method Using Deep Learning in Location‐Based Social Networks

Cited by 10 publications

References 34 publications

Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest Recommendation

Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest Recommendation

Spatio-Temporal Unequal Interval Correlation-Aware Self-Attention Network for Next POI Recommendation

POI Recommendation Model Using Multi-Head Attention in Location-Based Social Network Big Data

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