Real-world Location-based Social Networks Datasets Research Articles

Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over eleven state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.

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.

Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor Factorization

The rapid development of big data technology and mobile intelligent devices has led to the development of location-based social networks (LBSNs). To understand users’ behavioral patterns and improve the accuracy of location-based services, point-of-interest (POI) recommendation has become an important task. In contrast to the general task of product recommendation, POI recommendation faces the problems of the sparsity and weak semantics of user check-in data. To address these issues, an increasing number of studies have improved the accuracy of POI recommendations by introducing contextual information such as geographical, temporal, textual, and social relations. However, the rich context also brings great challenges to POI recommendation, such as the low utilization rate of context information, difficulty in balancing the richness of contextual information, and the complexity of the recommendation matrix. Considering that similar users have more interest preferences in common than users generally have, the check-in information of similar users has greater reference meaning. Thus, we propose a personalized POI recommendation method named CULT-TF, which incorporates similar users’ contextual information into the tensor factorization model. First, we present a user activity model and a user similarity model, which integrate contextual information to calculate the user activity and similarity between users. According to user activity, the most representative active users are selected as user clustering centers, and then users are clustered based on user similarity into several similar user clusters (C). Next, we construct a third-order tensor (user-location-time matrix) for each user cluster by using user activity, POI popularity, and time slot popularity as the eigenvalues in the user (U), location (L), and time (T) dimensions, and the eigenvalue of each dimension is modeled by integrating contextual information of users’ check-in behavior at the user, location, and time levels. Similar user clustering reduces the number of users in tensor modeling, reducing the U dimension. To further reduce the complexity of the recommendation matrix, the reduction of the L dimension is achieved through ROI (region of interest) clustering, and the reduction of the T dimension is achieved through time slot encoding. Then, we use tensor factorization (TF) to obtain the recommendation results. Our method decreases the complexity of the tensor matrix and integrates rich contextual information on users’ check-in behavior. Finally, we conducted a comprehensive performance evaluation of CULT-TF using real-world LBSN datasets from Brightkite. The experimental results show that our proposed method performs much better than other recommendation methods in terms of precision and recall.

Improving the spatial–temporal aware attention network with dynamic trajectory graph learning for next Point-Of-Interest recommendation

Next Point-Of-Interest (POI) recommendation aim to predict users’ next visits by mining their movement patterns. Existing works attempt to extract spatial–temporal relationships from historical check-ins; however, the following critical factors have not been adequately considered: (1) structured features implied in trajectory that reflect individual visit tendency; (2) collaborative signals from other users and (3) dynamic user preference. To this end, we jointly take into full consideration the graph-structured information as well as sequential effects of user trajectory sequences and propose the Trajectory Graph enhanced Spatial–Temporal aware Attention Network (TGSTAN). Given the general preference among users and the shifts of individual interests over time, we present a novel trajectory-aware dynamic graph convolution network module (TDGCN) to facilitate the capturing of local spatial correlations. Specifically, TDGCN dynamically adjusts the normalized adjacency matrix of the trajectory graph by element-wise multiplication with self-attentive POI representations. The local trajectory graph is generated from the same training batch to reflect real-time and collaborative signals, while also following causality. Moreover, we explicitly integrate spatial–temporal interval information with bilinear interpolation to comprehensively attach relative proximity to attention mechanism when capturing long-term dependence. Extensive experiments on three real-world Location-Based Social Networks datasets (Foursquare_TKY, Weeplaces and Gowalla_CA) demonstrate that the proposed TGSTAN consistently outperforms the existing state-of-the-art baselines with an average of 8.18%, 6.59%, and 9.60% improvement on the three datasets, respectively.

Sparse User Check-in Venue Prediction By Exploring Latent Decision Contexts From Location-Based Social Networks

The proliferation of online Location-Based Social Networks (LBSN) has offered unprecedented opportunities for understanding fine-grained spatio-temporal behaviors of users and developing new location-aware applications. In this article, we focus on the problem of “Sparse User Check-in Venue Prediction,” where the goal is to predict the next venue LBSN users will visit by exploiting their sparse online check-in traces and the latent decision contexts. While efforts have been made to predict users’ check-in traces on a LBSN, several important challenges still exist. First, check-in traces contributed by LBSN users are often too sparse to provide sufficient evidence for a reliable prediction, especially when the prediction space is huge (e.g., hundreds of thousands of venues in large cities). Second, the user's decision context on which venue to visit next is often latent and has not been incorporated by current venue prediction models. Third, the dynamic and non-deterministic dependency between check-ins is either ignored or replaced by a simplified “consecutiveness” assumption in existing solutions, leading to sub-optimal prediction results. In this article, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context-aware Sparse Check-in Venue Prediction (CSCVP)</i> scheme inspired by natural language processing techniques to address the above challenges. In particular, CSCVP predicts the venue category information and explores the similarity between users to address data sparsity challenge by significantly reducing the prediction space. It also leverages the Probabilistic Latent Semantic Analysis (PLSA) model to incorporate the user decision context into the prediction model. Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins. We evaluate CSCVP using three real-world LBSN datasets. The results show that our scheme significantly improves accuracy (30.9 percent improvement) of the state-of-the-art user check-in venue prediction solutions.

Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.

On Physical-Social-Aware Localness Inference by Exploring Big Data from Location-Based Services

A user's localness (i.e., whether a user is a local resident in a city or not) and a venue's local attractiveness (i.e., the likelihood of a venue to attract local people) are important information for many location-based applications related with Cyber-Physical Systems (CPS), such as participatory sensing, urban planning, traffic control and localized travel recommendations. Previous effort has been devoted to geo-locating users in a city using supervised learning approaches, which depend on the availability of high quality training datasets. However, it is difficult to obtain such training datasets in the real-world CPS applications due to the issue of privacy. In this work, we develop an unsupervised approach, called a Physical-Social-Aware Inference (PSAI) scheme, to jointly infer a user's localness and a venue's local attractiveness by exploring both the physical and social information embedded in the location-based social networks (LBSN). We further implement a parallel PSAI framework on the platform of a Graphic Processing Unit (GPU) to enhance its ability to process large-scale data. Our extensive experiments on the real-world LBSN datasets demonstrate the effectiveness and efficiency of the PSAI scheme compared to the state-of-the-art baselines.

MTPR: A Multi-Task Learning Based POI Recommendation Considering Temporal Check-Ins and Geographical Locations

The rapid development of location-based social networks (LBSNs) produces the increasing number of check-in records and corresponding heterogeneous information which bring big challenges of points-of-interest (POIs) recommendation in our daily lives. The emergence of various recommender techniques bridges the gap between the numerous heterogeneous check-ins and the personalized POI recommendation. However, due to the differences between LBSNs and conventional recommendation tasks, besides the user feedback, the spatio-temporal information is also significant to precisely capture the user preferences. In this paper, we propose a multi-task learning model based POI recommender system which exploits a structure of generative adversarial networks (GAN) simultaneously considering temporal check-ins and geographical locations. The GAN-based model is capable of relieving the sparsity of check-in data in POI recommender systems. The temporal check-ins not only present the preference but also show the lifestyle of an individual while the geographical locations describe the active region of users which further filters POIs far from the feasible region. The multi-task learning strategy is capable of combining the information of temporal check-ins and geographical locations to improve the performance of personalized POI recommendation. We conduct the experiments on two real-world LBSNs datasets and the experimental results show the effectiveness of our proposed approach.

Location-based social simulation for prescriptive analytics of disease spread

Human mobility and social networks have received considerable attention from researchers in recent years. What has been sorely missing is a comprehensive data set that not only addresses geometric movement patterns derived from trajectories, but also provides social networks and causal links as to why movement happens in the first place. To some extent, this challenge is addressed by studying location-based social networks (LBSNs). However, the scope of real-world LBSN data sets is constrained by privacy concerns, a lack of authoritative ground-truth, their sparsity, and small size. To overcome these issues we have infused a novel geographically explicit agent-based simulation framework to simulate human behavior and to create synthetic but realistic LBSN data based on human patterns-of-life (i.e., a geo-social simulation). Such data not only captures the location of users over time, but also their motivation, and interactions via temporal social networks. We have open sourced our framework and released a set of large data sets for the SIGSPATIAL community. In order to showcase the versatility of our simulation framework, we added disease a model that simulates an outbreak and allows us to test different policy measures such as implementing mandatory mask use and various social distancing measures. The produced data sets are massive and allow us to capture 100% of the (simulated) population over time without any data uncertainty, privacy-related concerns, or incompleteness. It allows researchers to see the (simulated) world through the lens of an omniscient entity having perfect data.

LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks

Location-Based Social Networks (LBSNs) have been widely used as a primary data source for studying the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users’ mobility homophily and social proximity, and show that mobility and social features can help friendship and location prediction tasks, respectively. However, these hand-crafted features not only require tedious human efforts, but also are difficult to generalize. Against this background, we propose in this paper LBSN2Vec++, a heterogeneous hypergraph embedding approach designed specifically for LBSN data for automatic feature learning. Specifically, LBSN data intrinsically forms a heterogeneous hypergraph including both user-user homogeneous edges (friendships) and user-time-POI-semantic heterogeneous hyperedges (check-ins). Based on this hypergraph, we first propose a random-walk-with-stay scheme to jointly sample user check-ins and social relationships, and then learn node embeddings from the sampled (hyper)edges by not only preserving the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -wise node proximity captured by the hyperedges, but also considering embedding space transformation between node domains to fully grasp the complex structural characteristics of the LBSN heterogeneous hypergraph. Using real-world LBSN datasets collected in six cities all over the world, our extensive evaluation shows that LBSN2Vec++ significantly and consistently outperforms both state-of-the-art graph embedding techniques by up to 68 percent and the best-performing hand-crafted features in the literature by up to 70.14 percent on friendship and location prediction tasks.

Adversarial Mobility Learning for Human Trajectory Classification

Understanding human mobility is one of the important but challenging tasks in Location-based Social Networks (LBSN). Recently, a user mobility mining task called Trajectory User Linking (TUL) has become an essential and popular topic, aiming at identifying user identities through exploiting their mobility patterns. Existing methods mainly focus on learning sequential mobility patterns by capturing long-short term dependencies among historical check-ins. However, users have personalized moving preferences, which have not been considered in previous work. Besides, how to leverage the prior knowledge behind human mobility needs to be further investigated. In this work, we present a novel semi-supervised method, called AdattTUL, to make adversarial mobility learning for human trajectory classification, which is an end-to-end framework modeling human moving patterns. AdattTUL integrates multiple human preferences of check-in behaviors and involves an attention mechanism to dynamically capture the complex relationships of user check-ins from trajectory data. In addition, AdattTUL leverages an adversarial network to help in regularizing the data distribution of human trajectories. Extensive experiments conducted on real-world LBSN datasets show that AdattTUL significantly improves the TUL performance.

Geography-Aware Inductive Matrix Completion for Personalized Point-of-Interest Recommendation in Smart Cities

With the development of Internet of Things technology, the will to make cities smarter is growing. In the context of smart cities, where people are surrounded by a tremendous number of points of interests (POIs), POI recommendation is of great significance. The massive amount of user check-in data collected by location-based social networks (LBSNs) can help users explore new places via POI recommendations. With rich auxiliary data becomeing available in LBSNs, purely exploiting users’ check-in information for POI recommendation is not sufficient. Although several attempts have been done to employ geographical influence to enhance POI recommendation, they simply use a certain distribution function to measure the geographical influence between POIs, which may lead to biased results. To this end, this article proposes a geography-aware inductive matrix completion (GAIMC) approach for personalized POI recommendation. Specifically, the GAIMC model consists of two parts, including geographic feature extraction via a Gaussian mixture model (GMM) and inductive matrix completion for recommendation. The GAIMC model first captures the geographical influence among users and POIs by the GMM, which can mine clustering information with hierarchical structures based on the users’ check-in data and POIs’ location information. Then, a matrix-completion method called inductive matrix completion, which can incorporate geographical features with the user-POI association metric, is utilized to recommend POIs. The experimental results on two real-world LBSN data sets demonstrate that our proposed model can achieve the best recommendation performance in comparison with the state-of-the-art counterparts.

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.

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top- k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.

Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks

Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive “home” location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.

A General Geographical Probabilistic Factor Model for Point of Interest Recommendation

The problem of point of interest (POI) recommendation is to provide personalized recommendations of places, such as restaurants and movie theaters. The increasing prevalence of mobile devices and of location based social networks (LBSNs) poses significant new opportunities as well as challenges, which we address. The decision process for a user to choose a POI is complex and can be influenced by numerous factors, such as personal preferences, geographical considerations, and user mobility behaviors. This is further complicated by the connection LBSNs and mobile devices. While there are some studies on POI recommendations, they lack an integrated analysis of the joint effect of multiple factors. Meanwhile, although latent factor models have been proved effective and are thus widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of LBSNs. To this end, in this paper, we propose a general geographical probabilistic factor model ( $\sf{Geo}$ -PFM) framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user’s check-in behavior. Also, user mobility behaviors can be effectively leveraged in the recommendation model. Moreover, based our $\sf{Geo}$ -PFM framework, we further develop a Poisson $\sf{Geo}$ -PFM which provides a more rigorous probabilistic generative process for the entire model and is effective in modeling the skewed user check-in count data as implicit feedback for better POI recommendations. Finally, extensive experimental results on three real-world LBSN datasets (which differ in terms of user mobility, POI geographical distribution, implicit response data skewness, and user-POI observation sparsity), show that the proposed recommendation methods outperform state-of-the-art latent factor models by a significant margin.

Mining Human Mobility in Location-Based Social Networks

In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to check at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., and where a user (who) has been to for what, corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mi ing and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to inv stigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.

Addressing the cold-start problem in location recommendation using geo-social correlations

Location-based social networks (LBSNs) have attracted an increasing number of users in recent years, resulting in large amounts of geographical and social data. Such LBSN data provide an unprecedented opportunity to study the human movement from their socio-spatial behavior, in order to improve location-based applications like location recommendation. As users can check-in at new places, traditional work on location prediction that relies on mining a user’s historical moving trajectories fails as it is not designed for the cold-start problem of recommending new check-ins. While previous work on LBSNs attempting to utilize a user’s social connections for location recommendation observed limited help from social network information. In this work, we propose to address the cold-start location recommendation problem by capturing the correlations between social networks and geographical distance on LBSNs with a geo-social correlation model. The experimental results on a real-world LBSN dataset demonstrate that our approach properly models the geo-social correlations of a user’s cold-start check-ins and significantly improves the location recommendation performance.