Location-based Social Network Services Research Articles

Using a Flexible Model to Compare the Efficacy of Geographical and Temporal Contextual Information of Location-Based Social Network Data for Location Prediction

In recent years, next location prediction has been of paramount importance for a wide range of location-based social network (LBSN) services. The influence of geographical and temporal contextual information (GTCI) is crucial for analyzing individual behaviors for personalized point-of-interest (POI) recommendations. A number of studies have considered GTCI to improve the performance of POI prediction algorithms, but they have limitations. Moreover, reviewing the related literature revealed that no research has investigated and evaluated the GTCI of LBSN data for location prediction in the form presented in this study. Here, we extended the gated recurrent unit (GRU) model by adding additional attention gates to separately consider GTCI for location prediction based on LBSN data and introduced the extended attention GRU (EAGRU) model. Furthermore, we used the flexibility of the EAGRU architecture and developed it in four states to compare the efficacy of GTCI for location prediction for LBSN users. Real-world, large-scale datasets based on two LBSNs (Gowalla and Foursquare) were used for a complete review. The results revealed that the performance of the EAGRU model was higher than that of competitive baseline methods. In addition, the efficacy of the geographical CI was significantly higher than the temporal CI.

A top-k POI recommendation approach based on LBSN and multi-graph fusion

POI(Point of Interest) recommendation is a basic and on-going issue in LBSN (Location-based Social Network) services. In this paper, a novel POI recommendation approach which is based on LBSN and multi-graph fusion is proposed. First, we take advantages of the graph neural network to construct user-POI interaction graph based on the rating data of users and construct user social graph based on the user social networks. First-order friends and high-order friends will be considered simultaneously in the user social graph. And then, we present a spectral cluster-based algorithm to gain the latent vector of the POI in location space. After this, the graph neural network is used to learn the information above. Lastly, we predict the score based on the aforementioned information and pick out the top-k POIs with the highest scores to form a recommendation list. Extensive experiments conducted on real datasets demonstrated that the method proposed in this paper can effectively generate the embedding vectors of users and POIs, and can achieve high recommendation accuracy as well.

POI Recommendation Method of Neural Matrix Factorization Integrating Auxiliary Attribute Information

Point-of-interest (POI) recommendation is the prevalent personalized service in location-based social networks (LBSNs). A single use of matrix factorization (MF) or deep neural networks cannot effectively capture the complex structure of user–POI interactions. In addition, to alleviate the data-sparsity problem, current methods primarily introduce the auxiliary information of users and POIs. Auxiliary information is often judged to be equally valued, which will dissipate some of the valuable information. Hence, we propose a novel POI recommendation method fusing auxiliary attribute information based on the neural matrix factorization, integrating the convolutional neural network and attention mechanism (NueMF-CAA). First, the k-means and term frequency–inverse document frequency (TF-IDF) algorithms are used to mine the auxiliary attribute information of users and POIs from user check-in data to alleviate the data-sparsity problem. A convolutional neural network and an attention mechanism are applied to learn the expression of auxiliary attribute information and distinguish the importance of auxiliary attribute information, respectively. Then, the auxiliary attribute information feature vectors of users and POIs are concatenated with their respective latent feature vectors to obtain the complete latent feature vectors of users and POIs. Meanwhile, generalized matrix factorization (GMF) and multilayer perceptron (MLP) are used to learn the linear and nonlinear interactions between users and POIs, respectively, and the last hidden layer is connected to integrate the two parts to alleviate the implicit feedback problem and make the recommendation results more interpretable. Experiments on two real-world datasets, the Foursquare dataset and the Weibo dataset, demonstrate that the proposed method significantly improves the evaluation metrics—hit ratio (HR) and normalized discounted cumulative gain (NDCG).

Privacy-preserving “Check-in Award” Service in Location-based Social Networks

Privacy-preserving “Check-in Award” Service in Location-based Social Networks

A Point-of-Interest Recommendation Method Exploiting Sequential, Category and Geographical Influence

Point of interest (POI) recommendation as an important service in location-based social networks has developed rapidly, which can help users find more interesting unknown locations and facilitate service providers to provide users with more accurate notifications or advertisements. Some existing work has addressed the data sparsity problem of collaborative filtering by incorporating contextual information into the model. However, they ignore the sequence relationship contained in the user’s historical check-in records, which makes it difficult to accurately model the user’s preference and affects the final recommendation results. To acquire users’ preference for a location more accurately, this paper proposes a new POI recommendation framework exploiting sequential, category, and geographical influence. Firstly, we obtain the latent vector of POI and the latent vector of the user’s preference for POI from the user’s check-in sequence based on the word embedding model. Next, a virtual common access sequence for users is constructed according to the user’s check-ins, a new similarity computation method is present combining category differentiation and POI latent vector. Then, we apply it to the collaborative filtering framework to get the user’s behavioral preference probability of POI. In addition, the kernel density estimation method is employed to get the user’s geographical preference probability of POI by considering the geographical influence. Finally, the POI recommendation list is obtained by the weighted fusion of the two users’ preference probability to improve the performance of the POI recommendation. Experimental results on two datasets indicate that the proposed method has better performance in terms of three evaluation metrics than the other five POI recommendation methods.

Do individuals disclose or withhold information following the same logic: a configurational perspective of information disclosure in social media

PurposeThis paper aimed to explain why individuals still tend to disclose their privacy information even when privacy risks are high and whether individuals disclose or withhold information following the same logic.Design/methodology/approachThis study develops a configurational decision tree model (CDTM) for precisely understanding individuals' decision-making process of privacy disclosure. A survey of location-based social network service (LBSNS) users was conducted to collect data, and fuzzy-set qualitative comparative analysis (fsQCA) was adopted to validate the hypotheses.FindingsThis paper identified two configurations for high and low disclosure, respectively, and found that the benefits and the risks did not function independently but interdependently, and the justice would play a crucial role when both the benefits and the risks were high. Furthermore, the authors found that there were asymmetric mechanisms for high disclosure and low disclosure, and males focused more on perceived usefulness, while females concerned more about perceived enjoyment, privacy risks and perceived justice.Originality/valueThis paper further extends privacy calculus model (PCM) and deepens the understanding of the privacy calculus process from a configurational perspective. In addition, this study also provides guidance for future research on how to adopt the configurational approach with qualitative comparative analysis (QCA) to revise and improve relevant theories for information systems (IS) behavioral research.

P3OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing.

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.

Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user’s home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others.

Point of interest recommendations based on the anchoring effect in location-based social network services

A point of interest (POI) recommender system (RS) is one of the representative research areas based on the location-based social network (LBSN). Most POI RS studies utilized various implicit information or social information to improve recommendation accuracy. However, majority of these studies overlooked the importance of users’ initial check-in information. Users are affected by their first input data in online services, and this phenomenon is called the anchoring effect. In POI RSs, few studies have analyzed the association with the anchoring effect while other RS domains already verified this effect. In particular, a research area, including POI RS, that focuses on the importance of the initial input does not exist. In this paper, we propose a latent Dirichlet allocation (LDA) model based on the anchoring effect for POI RS. This model emphasizes the importance of initial check-in data and is called the anchor-LDA. Experimental results showed that the anchor-LDA outperformed existing LDA-based POI recommender algorithms. Furthermore, we validated the importance of initial check-in information on the LBSN.

Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification

Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.

Social-aware spatial keyword top-k group query

With the increasing popularity of location-based social networking services, information in social networks has become an important basis for analyzing user preferences. However, the existing spatial keyword group query only focuses on the distance constraint between the user groups, and ignores the social relationship between the user and his friends, which may affect the query results. Therefore, in order to meet the diverse query needs of user groups and improve user satisfaction based on information in social networks, this paper proposes a social-aware spatial keyword top-k group query problem. This problem aims to retrieve a set of k groups of POI objects that satisfy the preferences of multiple users, taking into account spatial proximity, social relevance, and keyword constraints. To solve this problem, we first design a rank function to measure the correlation between the query set and the candidate set. Next, in order to improve the query efficiency, we develop a novel hybrid index structure, SAIR-tree, which comprehensively considers the attributes of social, spatial, and textual. Then, we propose an approximate algorithm and an exact algorithm, combining with the pruning strategy, can efficiently search the top-k result set. Finally, experiments on real dataset confirm the efficiency and accuracy of the proposed algorithms.

Cohesive Group Nearest Neighbor Queries on Road-Social Networks under Multi-Criteria

The group nearest neighbor (GNN) search on a road network <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , has been extensively studied; however, previous works neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Meanwhile, the ratings of spatial objects can also be used as recommended guidelines. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study two new problems: (1) a GNN search on a road network that incorporates cohesive social relationships (CGNN) and (2) a CGNN query under multi-criteria (MCGNN). Specifically, both the query users of highest closeness and the corresponding top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are retrieved. To address critical challenges on the effectiveness of results and the efficiency of computation over large road-social networks: (1) for CGNN, we propose a filtering-and-verification framework. During filtering, we prune substantial unpromising users and objects using social and geospatial constraints. During verification, we obtain the object candidates, among which the top <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> are selected, with respect to the qualified users; (2) for MCGNN, we propose threshold-based selection and expansion strategies, where different strict boundaries are proposed to ensure that correct top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are found early. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.

A point-of-interest suggestion algorithm in Multi-source geo-social networks

Newly emerging location-based social network (LBSN) services provide us with new platforms to share interests and individual experience based on their activity history. The problems of data sparsity and user distrust in LBSNs create a severe challenge for traditional recommender systems. Moreover, users’ behaviors in LBSNs show an obvious spatio-temporal pattern. Valuable extra information from microblog-based social networks (MBSNs) can be utilized to improve the effectiveness of POI suggestion. In this study, we propose a latent probabilistic generative model called MTAS, which can accurately capture the underlying information in users’ words extracted from both LBSNs and MBSNs by taking into consideration the decision probability, a latent variable indicating a user’s tendency to publish a review in LBSNs or MBSNs. Then, the parameters of the MTAS model can be inferred by the Gibbs sampling method in an effective manner. Based on MTAS, we design an effective framework to fulfill the top-k suggestion. Extensive experiments on two real geo-social networks show that MTAS achieves better performance than existing state-of-the-art methods.

Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.

GLR: A graph-based latent representation model for successive POI recommendation

Point-of-Interest (POI) recommendation is an important service in location-based social networks (LBSNs) since it can help a user to discover new POIs for future visiting. In order to provide better recommendation experience, a novel POI recommendation paradigm, named successive POI recommendation, has been proposed. The difference between traditional POI recommendation and successive POI recommendation is that successive POI recommendation focuses on recommending POIs that the target user may like to visit within a time period (e.g., a few hours). To address this problem, we propose a new graph-based latent representation model called GLR, to obtain the latent vectors of temporal successive transition influence and temporal user preference based on the historical check-in records. We also propose a novel method named GLR_GT to employ these latent vectors and geographical influence of POIs to perform successive POI recommendation. Moreover, we also propose an extended method named GLR_GT_LSTM to employ a long short-term memory (LSTM) neural network to model users’ complex transition behavior. Several experiments are conducted on some real-world LBSN datasets. Experimental results show that our proposed method GLR_GT_LSTM outperforms the other prior successive POI recommendation methods in terms of precision and recall.

Road network-based region of interest mining and social relationship recommendation

Region of interest (ROI) discovery is among the most common functions in location-based social networking services (LBSNS). While former researches mainly utilize the accurate location coordinates history, the road context-based active region extraction algorithm (RAREA) proposed in this paper explores the method to extract those regions with road contexts. Furthermore, based on the active regions extracted by RAREA, the kNN consistency-based relationship recommendation algorithm (kNNC-RRA) is proposed as well. The kNNC-RRA compares the similarity degree of the active regions among the users to find the individuals with similar preferences to recommend the potential relationships. Experimental results illustrate that by analyzing the characteristics of those road contexts, ROIs are able to be discovered with high efficiency. And our work shows that both privacy protection and personalized services can be achieved in LBSNS.

Recommendations based on user effective point-of-interest path

Point-of-interest (POI) recommendation has become an important service in location-based social networks. Existing recommendation algorithms provide users with a diverse pool of POIs. However, these algorithms tend to generate a list of unrelated POIs that user cannot continuously visit due to lack of appropriate associations. In this paper, we first proposed a concept that can recommend POIs by considering both category diversity features of POIs and possible associations of POIs. Then, we developed a top-k POI recommendation model based on effective path coverage. Moreover, considering this model has been proven to be a NP-hard problem, we developed a dynamic optimization algorithm to provide an approximate solution. Finally, we compared it with two popular algorithms by using two real-world datasets, and found that our proposed algorithm has better performance in terms of diversity and precision.

Efficient and Privacy-Preserving Proximity Detection Schemes for Social Applications

With the pervasiveness of location-aware mobile terminals and the popularity of social applications, location-based social networking service (LBSNS) has brought great convenience to people's life. Meanwhile, proximity detection, which makes LBSNS more flexible, has aroused widespread concern. However, the prosperity of LBSNS still faces many severe challenges on account of users' location privacy and data security. In this paper, we propose two efficient and privacy-preserving proximity detection schemes, named arbitrary geometric range query for polygons (AGRQ-P) and arbitrary geometric range query for circles (AGRQ-C), for location-based social applications. With proposed schemes, a user can choose any area on the map, and query whether her/his friends are within the region without divulging the query information to both social application servers and other users, meanwhile, the accurate locations of her/his friends are also confidential for the servers and the query user. Specifically, with algorithms based on ciphertext of geometric range query, users' query and location information is blurred into ciphertext in client, thus no one but the user knows her/his own sensitive information. Detailed security analysis shows that various security threats can be defended. In addition, the proposed schemes are implemented in an IM APP with a real LBS dataset, and extensive simulation results over smart phones further demonstrate that AGRQ-P and AGRQ-C are highly efficient and can be implemented effectively.

Personalized itinerary recommendation with time constraints using GPS datasets

Planning a personalized itinerary for an unfamiliar region requires much effort to design desirable travel plans. With the rapid development of location-based social network (LBSN) services, data mining techniques are utilized to retrieve useful information such as geographical features and social relationships. In this paper, we propose a personalized itinerary recommendation with time constraints (pirT) framework for the LBSN by exploiting geographical features and social relationships to recommend a personalized itinerary that satisfies user preferences (i.e., travel behaviors). In pirT, we have designed a user-based collaborative filtering with time preference (UTP) to explore user preferences by considering the visiting time of locations which the users have visited in our framework. UTP allows a tourist to find users with similar travel behaviors to those of the service requester in the past and to recommend interesting locations in the itineraries that these similar users have traveled to before. Subsequently, given a beginning location and a destination with a time constraint specified by the tourist, we devise the top-k $$A^*$$ search-based recommendations and re-ranking itinerary candidate algorithms to efficiently plan the top k personalized itineraries. In the planning process, we simultaneously take account of the visiting time of locations, the transit time between locations, and the order of visiting locations. We conducted our experiments on the Gowalla dataset and demonstrated the effectiveness of our pirT framework comparing it with the personalized trip recommendation (PTR) framework. The results show that our pirT framework is superior to the PTR framework.

Predicting user-level marketing performance of location-based social networking sites

ABSTRACTThis research aims at developing a framework for evaluating the user-level marketing performance of location-based social network services. Based on DeLone and McLean’s electronic commerce success model, we proposed a conceptual model to measure the marketing success of a firm participating in location-based services (LBSs). In order to test the research model proposed, we conducted an empirical study where we gathered data from a questionnaire survey using 254 current users of location-based social networking sites. The results of data analysis reveal that information quality, system quality, and trust have no significant effects on intention to use LBSs, but significant effects on user satisfaction. Furthermore, findings indicate that user satisfaction has a significant effect on marketing performance, whereas intention to use LBSs has no such effect. We suggested implications and future research directions based on these findings.