Location-based Social Networks Research Articles

Privacy-Preserving Point-of-Interest Recommendation based on Simplified Graph Convolutional Network for Geological Traveling

The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can can be utilized to mine their preferences, thereby facilitating the promotion of the geological tourism industry. However, such check-ins often contain sensitive user information that poses privacy leakage risks. To address this issue, some methods have been proposed to develop privacy-preserving point-of-interest (POI) recommendation systems. These methods commonly rely on either perturbation-based or federated learning techniques to protect users’ privacy. However, the former can hinder preference capture, while the latter remains vulnerable to privacy breaches during the parameter-sharing process. To overcome these challenges, we propose a novel privacy-preserving POI recommendation model that incorporates users’ privacy preferences based on a simplified graph convolutional neural network. Specifically, we employ a generative model to create a subset of POIs that reflect users’ preferences but do not reveal their private information, and then design a simplified graph convolutional network to analyze the high-order connectivity between users and POIs that are privacy-preserving. The resulting model enables efficient POI recommendation under strict privacy protection, which is particularly relevant to geological tourism. Experimental results on two public datasets demonstrate the effectiveness of our proposed approach.

Improving Next Location Recommendation Services With Spatial-Temporal Multi-Group Contrastive Learning

Next location recommendation services play a pivotal role in Location-Based Social Networks (LBSNs) due to their ability to provide personalized recommendations of attractive destinations, resulting in substantial benefits for both users and service providers. Recent research indicates that these services are influenced by both sequential and geographical factors. However, we argue that most of these services fail to fully exploit the latent multi-group knowledge of location semantics and user preferences, resulting in suboptimal performance. Therefore, we propose STMGCL, a novel spatial-temporal multi-group contrastive learning-based method to discover intrinsic multi-group information for improving next location recommendation services. Specifically, STMGCL designs Spatial Group Contrastive Learning (SGCL) to extract multiple group knowledge regarding location semantics. Additionally, it develops Temporal Group Contrastive Learning (TGCL) to explore multiple user preference group information through a self-attention based encoder. Finally, we leverage a multi-task learning strategy and a generalized Expectation Maximization (EM) algorithm to ensure that STMGCL is optimized end-to-end with guaranteed convergence. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of STMGCL over baselines.

Zone-Enhanced Spatio-Temporal Representation Learning for Urban POI Recommendation

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers' complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ime-z <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> ne-space <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> OI embedding model ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ToP</b> ), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers' POI check-in actions.

Modified node2vec and attention based fusion framework for next POI recommendation

The rise of location-based services has led to the widespread adoption of location-based social networks (LBSNs), which play a vital role in making recommendations for the next Point-of-Interest (POI). This paper introduces a modified node2Vec and attention-based fusion framework for the next POI recommendation. We start by preprocessing the raw data to gather the relevant information and present a modified node2vec algorithm to generate the feature vectors for users and locations. These feature vectors are then processed using the attention-based framework. The processed features are then used to create well-labeled and balanced datasets which are grouped by specific time intervals. These datasets are then used for training various ML classifiers which are ensembled in a weighted manner to make an improved fusion based recommendation system. The intensive experimental simulations demonstrate the effectiveness of the proposed framework over existing state-of-art methods.

SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation

The expansion of available information in location-based social networks (LBSNs) has led to information overload, making it urgent to discover users’ next point-of-interest (POI). Some existing works only consider certain modal information in LBSNs and do not transform them into high-dimensional structures, which hinders the alleviation of the data sparsity problem. Moreover, many approaches rely solely on social relationships, making it difficult to recommend POIs to new users without association information. To tackle these challenges, we propose a social- and spatial–temporal-aware next point-of-Interest (SSTP) recommendation model. SSTP uses two feature encoders based on self-attention mechanism and gate recurrent unit to model users’ check-in enhancement sequence hierarchically. We also design a random neighborhood sampling approach to mine user social relationships, thus alleviating the user cold start problem. Finally, we propose a geographical-aware graph attention network to learn the sensitivity of users to distance. Extensive experiments on two real-world datasets show that SSTP outperforms state-of-the-art models, improving Hit@k by 2.26–6.55% and MAP@k by 3.49–6.55%. Moreover, SSTP has better performance on sparse data, with an average improvement of 6.09% on the Hit@k. The code can be downloaded at https://github.com/Rih0/sstp.

Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network

Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.

Trust-aware spatial–temporal feature estimation for next POI recommendation in location-based social networks

Trust-aware spatial–temporal feature estimation for next POI recommendation in location-based social networks

Location Recommendation Based on Mobility Graph With Individual and Group Influences

With the rapid development of mobile technology, it is very convenient to share people's current locations by checking-in on Location-Based Social Networks (LBSNs). Using users' check-in histories to study mobility preferences and recommend new locations is a typical application to LBSNs. Most existing models explore reasonable representations for users and locations. However, a lack of behavioral mobility modeling would hamper a better understanding of users' mobility patterns. This paper proposes a location recommendation model to serve the personalized LBSNs application, called Spatio-temporal Individual mobility graph encoding network with Group Mobility Assistance (SIGMA). We design a spatio-temporal interaction enhanced graph neural network to encode the mobility graphs to represent individual mobility behaviors. Furthermore, we provide a novel stacked scoring approach to generate the recommendation score by combining the stacked individual mobility graphs with the group influences. We conduct extensive experiments on two real-world LBSNs data, Foursquare and Gowalla. The result demonstrates SIGMA outperforms ten state-of-the-art models and further confirms that both the individual and the group mobility behaviors play essential roles in the practical scenario of location recommendation.

CP-Link: Exploiting Continuous Spatio-Temporal Check-In Patterns for User Identity Linkage

Driven by the large amount of spatio-temporal data obtained from location-based social networks, the implementation of cross-domain user linkage, also known as the User Identity Linkage (UIL), has attracted increasing research attentions. While most of the existing UIL works discretize the spatio-temporal sparse data when identifying encountering or co-located events for UIL, user's distinctive behavior patterns implicit in the ‘`check-in’' spatio-temporal data with continuous nature pave the way for enhancing UIL performance. In this paper, we propose an approach dubbed {\it CP-Link} that exploits user behavior patterns in a continuous way. In CP-Link, the continuous space is divided into irregularly shaped stay regions, and a continuous time-based improved dynamic time warping (IDTW) method is proposed to calculate the similarity. {\color{blue}To bridge the gap between the ideal scenario with ample records and the reality with sparse data}, we adopt the user-associated location frequent pattern (LFP) model to compensate for the sparse deficiency. Extensive experiments conducted on real-world datasets demonstrate the effectiveness and superiority of CP-Link, which outperforms the state of the arts by more than 20% in terms of the AUC.

Next Point-of-Interest Recommendation Based on Joint Mining of Spatial–Temporal and Semantic Sequential Patterns

With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user’s check-in behavior at the current moment by analyzing and mining the correlations between the user’s check-in behaviors within his/her historical trajectory and then recommending the POI that the user is most likely to visit at the next time step. However, the user’s check-in trajectory presents extremely irregular sequential patterns, such as spatial–temporal patterns, semantic patterns, etc. Intuitively, the user’s visiting behavior is often accompanied by a certain purpose, which makes the check-in data in LBSNs often have rich semantic activity characteristics. However, existing research mainly focuses on exploring the spatial–temporal sequential patterns and lacks the mining of semantic information within the trajectory, so it is difficult to capture the user’s visiting intention. In this paper, we propose a self-attention- and multi-task-based method, called MSAN, to explore spatial–temporal and semantic sequential patterns simultaneously. Specifically, the MSAN proposes to mine the user’s visiting intention from his/her semantic sequence and uses the user’s visiting intention prediction task as the auxiliary task of the next POI recommendation task. The user’s visiting intention prediction uses hierarchical POI category attributes to describe the user’s visiting intention and designs a hierarchical semantic encoder (HSE) to encode the hierarchical intention features. Moreover, a self-attention-based hierarchical intention-aware module (HIAM) is proposed to mine temporal and hierarchical intention features. The next POI recommendation uses the self-attention-based spatial–temporal-aware module (STAM) to mine the spatial–temporal sequential patterns within the user’s check-in trajectory and fuses this with the hierarchical intention patterns to generate the next POI list. Experiments based on two real datasets verified the effectiveness of the model.

Uncertainty-Aware Heterogeneous Representation Learning in POI Recommender Systems

Discovering interesting yet unvisited point-of-interests (POIs) is among the most practical applications but challenging problems in location-based social networks (LBSNs). Popular approaches face several issues, such as data sparsity and difficulties in modeling latent nonlinearity between users and POIs. Furthermore, the uncertainty in LBSNs poses additional obstacles to learning good representations of users’ general and current interests. To effectively address these issues, we postulate that fusing multiple sources of information is paramount. Toward that, we propose a novel deep generative recommender system—Wasserstein autoencoder for POI recommendation (WaPOIR). It unifies the information from users’ personal preference, social influence, and geographical data, and captures users’ general interests from historical check-ins, while modeling users’ current interests from recently visited POIs. Unlike previous methods, WaPOIR learns the latent distribution of data in the Wasserstein space as a potential representation for each POI and each user in LBSNs. This enables simultaneous maintenance of social and POI interactions and modeling the uncertainty of their relationships. WaPOIR is a stochastic recommendation approach that allows Bayesian inference and approximation of variational posterior distribution. Extensive experiments conducted on real-world LBSN datasets demonstrate that WaPOIR achieves better performance over the state-of-the-art approaches.

STA-TCN: Spatial-temporal Attention over Temporal Convolutional Network for Next Point-of-interest Recommendation

Recent years have witnessed a vastly increasing popularity of location-based social networks (LBSNs), which facilitates studies on the next Point-of-Interest (POI) recommendation problem. A user’s POI visiting behavior shows the sequential transition correlation with previous successive check-ins and the global spatial-temporal correlation with those check-ins that happened a long time ago at a similar time of day and in geographically close areas. Although previous POI recommendation methods attempted to capture these two correlations, several limitations remain to be solved: (1) RNNs are widely adopted to capture the sequential transition correlation, whereas training an RNN is rather time-consuming given the long input check-in sequence. (2) The pairwise proximities on time of day and geographical area of check-ins are crucial for global spatial-temporal correlation learning, but have not been comprehensively considered by previous methods. To tackle these issues, we propose a novel next POI recommendation framework named STA-TCN. Specifically, instead of RNNs, STA-TCN augments the Temporal Convolutional Network with gated input injection to learn sequential transition correlation. Furthermore, STA-TCN fuses two novel grid-difference and time-sensitivity learning mechanisms with attention network to learn the pairwise spatial-temporal proximities among a user’s check-ins. Extensive experiments are conducted on two large-scale real-world LBSN datasets, and the results show that STA-TCN outperforms the best state-of-the-art baseline with an average improvement of 9.71% and 7.88% on hit rate and normalized discounted cumulative gain, respectively.

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants’ behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user’s activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people’s circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users’ inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion.

Lady and the Tramp Nextdoor: Online Manifestations of Real-World Inequalities in the Nextdoor Social Network

From health to education, income impacts a huge range of life choices. Earlier research has leveraged data from online social networks to study precisely this impact. In this paper, we ask the opposite question: do different levels of income result in different online behaviors? We demonstrate it does. We present the first large-scale study of Nextdoor, a popular location-based social network. We collect 2.6 Million posts from 64,283 neighborhoods in the United States and 3,325 neighborhoods in the United Kingdom, to examine whether online discourse reflects the income and income inequality of a neighborhood. We show that posts from neighborhoods with different incomes indeed differ, e.g. richer neighborhoods have a more positive sentiment and discuss crimes more, even though their actual crime rates are much lower. We then show that user-generated content can predict both income and inequality. We train multiple machine learning models and predict both income (R2=0.841) and inequality (R2=0.77).

DPTP-LICD: A differential privacy trajectory protection method based on latent interest community detection

With the rapid development of high-speed mobile network technology and high-precision positioning technology, the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social Networks. Researchers can mine users’ trajectories in Location-based Social Networks to obtain sensitive information, such as friendship groups, activity patterns, and consumption habits. Therefore, mobile users’ privacy and security issues have received growing attention in Location-based Social networks. It is crucial to strike a balance between privacy protection and data availability. This paper proposes a differential privacy trajectory protection method based on latent interest community detection(DPTP-LICD), ensuring strict privacy protection standards and user data availability. Firstly, based on the historical trajectory information of users, spatiotemporal constraint information is extracted to construct a potential community strength model for mobile users. Secondly, the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory, and their priorities are assigned based on a popularity model. A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data unusable. Finally, to prevent privacy leakage, we add Laplace and exponential noise in generating preferred hot spots and recommending user interest points. Security and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’ privacy from disclosure.

Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams

Online POI recommendation strives to recommend future visitable places to users over time. It is crucial to improve the user experience of location-based social networking applications (e.g., Google Maps, Yelp). While numerous studies focus on capturing user visit preferences, geo-human interactions are ignored. However, users make visit decisions based on the status of geospatial contexts. In the meantime, such visits will change the status of geospatial contexts. Therefore, disregarding such geo-human interactions in streams will result in inferior recommendation performance. To fill this gap, in this paper, we propose a novel deep interactive reinforcement learning framework to model geo-human interactions. Specifically, this framework has two main parts: the representation module and the imitation module. The purpose of the representation module is to capture geo-human interactions and convert them into embedding vectors (state). The imitation module is a reinforced agent whose job is to imitate user visit behavior by recommending next-visit POI (action) based on the state. Imitation performance is regarded as a reward signal to optimize the whole interactive framework. When the model converges, the imitation module can precisely perceive users and geospatial contexts to provide accurate POI recommendations. Finally, we conduct extensive experiments to validate the superiority of our framework.

Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation

Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role in smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current recommendation methods are based on learning the users’ check-in history and the users’ relationship network in the social network to make recommendations.Therefore, urban crowds’ regular travel patterns cannot be effectively utilized. In this paper, we propose a POI recommendation algorithm (HMRec) based on prior knowledge of human mobility patterns to solve this problem. Specifically, we propose the Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors for human mobility patterns. The framework incorporates attention mechanisms to capture spatio-temporal information in urban traffic patterns. HMPE employs downstream tasks and design upsampling modules to reconstruct representation vectors for task objectives, enabling end-to-end training of the framework and obtaining pre-trained parameters for the human mobility pattern extractor. Furthermore, we introduce the Human Mobility Recommendation (HMRec) algorithm, which improves feature cross-interactions in the breadth model and incorporates prior knowledge of human patterns. This ensures that the recommendation results align more closely with human travel patterns in urban environments. Comparative experiments conducted on the Foursquare dataset demonstrate that HMRec outperforms baseline models with an average performance improvement of approximately 3%. Finally, we discuss existing challenges and future research directions, including approaches to address the issue of data sparsity.

Preference-aware Bayesian Personalized Ranking for Point-of-interest recommendation

Point-of-interest (POI) recommendation has become one of the research highlights in the field of recommender systems due to the prosperity of location-based social networks in recent years. Various techniques have been proposed to improve the performance of the personalized recommendation service. Embedding-based methods have shown promising effect and attracted great attention for their flexibility and efficiency. Bayesian Personalized Ranking (BPR), as a famous optimization algorithm, has been widely used to learning the parameters of Embedding-based models in the recommendation scenario. However, existing Bayesian Personalized Ranking and its follow-up methods ignore the unique user preference when constructing the positive and negative samples, leading a suboptimal performance. To overcome this limitation, we propose a novel method named preference-aware Bayesian Personalized Ranking (PABPR) according to empirical analyses on real-world datasets. The empirical analyses show that a user tend to visit a POI with categories which have been visited before. Thus, the key idea of PABPR is to introduce such user behaviors into the sample constructing process. PABPR is a general method which could be used for training various Embedding methods. Extensive experiments show that PABPR can lead a superior model performance compare to BPR and its variant methods.

EPT-GCN: Edge propagation-based time-aware graph convolution network for POI recommendation

In location-based social networks (LBSNs), point-of-interest (POI) recommendation systems help users identify unvisited POIs by filtering large amounts of information. Accurate POI recommendations can effectively improve user satisfaction and save time in finding POIs. In recent years, the graph convolution network (GCN) technique, which enhances the representational ability of neural networks by learning the embeddings of users and items, has been widely adopted in recommendation systems to improve accuracy. Combining GCN with various information, such as time and geographical information, can further improve recommendation performance. However, existing GCN-based techniques simply adopt time information by modeling users’ check-in sequences, which is insufficient and ignores users’ time-based high-order connectivity. Note that time-based high-order connectivity refers to the relationship between indirect neighbors with similar preferences in the same time slot. In this paper, we propose a new time-aware GCN model to extract rich collaborative signals contained in time information. Our work is the first to divide user check-ins into multiple subgraphs, i.e., time slots, based on time information. We further propose an edge propagation module to adjust edge affiliation, where edges represent check-ins, to propagate user’s time-based preference to multiple time slots. The propagation module is based on an unsupervised learning algorithm and does not require additional ground-truth labels. Experimental results confirm that our method outperforms state-of-the-art GCN models in all baselines, improving Recall@5 from 0.0803 to 0.0874 (8.84%) on the Gowalla dataset and from 0.0360 to 0.0388 (7.78%) on the New York dataset. The proposed subgraph mining technique and novel edge-based propagation module have high scalability and can be applied to other subgraph construction models.

Decentralized Gradient-Quantization Based Matrix Factorization for Fast Privacy-Preserving Point-of-Interest Recommendation

With the rapidly growing of location-based social networks, point-of-interest (POI) recommendation has been attracting tremendous attentions. Previous works for POI recommendation usually use matrix factorization (MF)-based methods, which achieve promising performance. However, existing MF-based methods suffer from two critical limitations: (1) Privacy issues: all users’ sensitive data are collected to the centralized server which may leak on either the server side or during transmission. (2) Poor resource utilization and training efficiency: training on centralized server with potentially huge low-rank matrices is computational inefficient. In this paper, we propose a novel decentralized gradient-quantization based matrix factorization (DGMF) framework to address the above limitations in POI recommendation. Compared with the centralized MF methods which store all sensitive data and low-rank matrices during model training, DGMF treats each user’s device (e.g., phone) as an independent learner and keeps the sensitive data on each user’s end. Furthermore, a privacy-preserving and communication-efficient mechanism with gradient-quantization technique is presented to train the proposed model, which aims to handle the privacy problem and reduces the communication cost in the decentralized setting. Theoretical guarantees of the proposed algorithm and experimental studies on real-world datasets demonstrate the effectiveness of the proposed algorithm.