Personalized Point-of-interest Research Articles

Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN

With the intention of addressing the concern that existing point of interest recommendation methods fail to fully utilize the auxiliary information of the point of interest, from which it is challenging to extricate a substantial quantity of deeper feature information, a personalized point of interest (POI) recommendation model using Context-Aware Gated Recurrent Unit (CAGRU) and implicit semantic feature extraction was proposed. First, the check-in data is divided into five tags, and the continuous geographical location check-in data and time data are discretized. Then, the CAGRU was used to obtain the POI check-in features. Finally, the time sequence location information, user information and target location information are transformed through the nonlinear activation function to obtain the score of each location in the data set as the next POI location, and the Top-N recommendation is generated through the score. Experiments indicated that the results of the suggested method were better than the comparative methods.

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users.

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Aiming at the problems resulting from the fact that the existing point of interest (POI) recommendation methods cannot effectively consider the personalized differences of users' mobile behavior in space and time, the author proposes a personalized POI recommendation method using attention-based time sequence and distance contexts gated recurrent unit (ATSD-GRU). First, the author combined the time sequence and distance context with the GRU to extract useful information from users, effectively alleviating the data sparsity. Second, inspired by the attention mechanism, the author introduced the attention model further into the neural network to capture the user's main mobile behavior intention. Finally, the author studied the ATSD-GRU and trained through Bayesian personalized sorting framework and back propagation algorithm. Experiments imply that the proposed method outperforms the comparison method in terms of the F1 index for any recommended number. When the recommendation list length is 15, the proposed algorithm exhibits an accuracy of 9.23% and a recall rate of 14.65%, both higher than the compared algorithm.

Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation.

A personalized point-of-interest (POI) recommender system is of great significance to facilitate the daily life of users. However, it suffers from some challenges, such as trustworthiness and data sparsity problems. Existing models only consider the trust user influence and ignore the role of the trust location. Furthermore, they fail to refine the influence of context factors and fusion between the user preference and context models. To address the trustworthiness problem, we propose a novel bidirectional trust-enhanced collaborative filtering model, which investigates the trust filtering from the views of users and locations. To tackle the data sparsity problem, we introduce temporal factor into the trust filtering of users as well as geographical and textual content factors into the trust filtering of locations. To further alleviate the sparsity of user-POI rating matrices, we employ a weighted matrix factorization fused with the POI category factor to learn the user preference. To integrate the trust filtering models and the user preference model, we develop a fused framework with two kinds of integrating methods in relation to the different impacts of factors on the POIs that users have visited and the POIs that users have not visited. Finally, we conduct extensive experiments on Gowalla and Foursquare datasets to evaluate our proposed POI recommendation model, and the results show that our proposed model improves by 13.87% at precision@5 and 10.36% at recall@5 over the state-of-the-art model, which demonstrates that our proposed model outperforms the state-of-the-art method.

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.

Where have you been: Dual spatiotemporal-aware user mobility modeling for missing check-in POI identification

The prevalence of Location-based Social Networks (LBSNs) services makes next personalized Point-of-Interest (POI) predictions become a trending research topic. However, due to device failure or intention camouflage, geolocation information missing prevents existing POI-oriented researches for advanced user preference analysis. To this end, we propose a novel model named Bi-STAN, which fuses bi-direction spatiotemporal transition patterns and personalized dynamic preferences, to identify where the user has visited at a past specific time, namely missing check-in POI identification. Furthermore, to relieve data sparsity issues, Bi-STAN explicitly exploits spatiotemporal characteristics by doing bilateral traceback to search related items with high predictive power from user mobility traces. Specifically, Bi-STAN introduces (1) a temporal-aware attention semantic category encoder to unveil the latent semantic category transition patterns by modeling temporal periodicity and attenuation; (2) a spatial-aware attention POI encoder to capture the latent POI transition pattern by modeling spatial regularity and proximity; (3) a multitask-oriented decoder to incorporate personalized and temporal variance preference into learned transition patterns for missing check-in POI and category identification. Based on the complementarity and compatibility of multi-task learning, we further develop Bi-STAN with a self-adaptive learning rate for model optimization. Experimental results on two real-world datasets show the effectiveness of our proposed method. Significantly, Bi-STAN can also be adaptively applied to next POI prediction task with outstanding performances.

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.

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.

Efficient point-of-interest recommendation with hierarchical attention mechanism

Personalized Point-of-Interest (POI) Recommendation is very important for application platforms based on Location Based Social Networks (LBSNs). It can assist users in making decisions to alleviate the problem of information overload, and can also improve the user experience of these platforms and advance platform operators achieve personalized and accurate advertising. However, there exist some problems of data sparseness and cold start for a single user, and it is also difficult to mine valuable long-tailed POIs, although the size of the check-in data is large. Therefore, in order to address the above problems, we propose a personalized POI Recommendation approach based on Hierarchical Attention Mechanism (HAM-POIRec) which can effectively increase data utilization. Firstly, we define the concepts of explicit features and implicit features, which pave the ideas of selecting data and computational models for POI recommendation based on machine learning. Secondly, we propose a hierarchical attention mechanism with the structure of local-to-global, which extracts contributions and mines more hidden information from individual features, combination features, and overall features. Finally, we present the Natural Language Processing (NLP)-based “User-POI” matching mechanism for the first time in the field of POI recommendation to improve the recommendation accuracy by fine-tuning the POIs predicted by the recommendation system. Extensive experiments are conducted for demonstrating that the HAM-POIRec method outperforms state-of-the-art DeepPIM method and the other comparison methods (SAE-NAD, MGMPFM and LRT), especially in predicting sequence POIs and solving cold start problem.

A personalized point-of-interest recommendation system for O2O commerce

Online-to-offline (O2O) commerce, e.g., the internet celebrity economy, provides a seamless service experience between online commerce and offline bricks-and-mortar commerce. This type of commerce model is closely related to location-based social networks (LBSNs), which incorporate mobility patterns and human social ties. Personalized point-of-interest (POI) recommendations are crucial for O2O commerce in LBSNs; such recommendations not only help users explore new venues but also enable many location-based services, e.g., the targeting of mobile advertisements to users. However, producing personalized POI recommendations for O2O commerce is highly challenging, since LBSNs involve heterogeneous types of data and the user-POI matrix is very sparse. LBSNs have substantially altered how people interact by sharing a wide range of user information, such as the products and services that users use and the places and events that users visit. To address these challenges in O2O commerce LBSNs, we analyze users’ check-in behaviors in detail and introduce the concept of a heterogeneous information network (HIN). Then, we propose a HIN-based POI recommendation system, which consists of two components: an improved singular value decomposition (SVD++) and factorization machines (FMs). The results of experiments on two real-world O2O commerce websites, namely, Gowalla and Foursquare, demonstrate that our method is more accurate than baseline methods. Additionally, a case study of the bricks-and-mortar brand of internet celebrity indicates that our proposed POI recommendation system can be used to conduct online promotion and purchasing to drive offline marketing and consumption.

Point-of-Interest Recommendation Based on User Contextual Behavior Semantics

By suggesting new visiting places, 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 data sparsity and complexity of user check-in behavior still pose big challenges to accurate personalized POI recommendation. To tackle these problems, in this paper, we propose a POI recommendation model named HeteGeoRankRec based on user contextual behavior semantics. First, we employ the meta-path of heterogeneous information network (HIN) to represent the complex semantic relationship among users and POIs. Second, we introduce different context constraints (such as time and weather) into the meta-path, to reveal the fine-grained user behavioral features. Afterwards, we propose a weighted matrix factorization model which considers the influence of geographical distance through the user–POI semantic correlativity matrices generated by multiple meta-paths. Finally, we present a fusion method based on learning to rank, which unifies the recommendation results of different meta-paths as the final user preference. The experiments on the real data collected from Foursquare demonstrate that HeteGeoRankRec has the better performance than the state-of-the-art baselines.

Geographic Diversification of Recommended POIs in Frequently Visited Areas

In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is especially important when POIs are recommended in the target users’ frequently visited areas, because users are likely to revisit such areas. In addition to the (POI) category diversity that is a popular diversification objective in recommendation domains, diversification of recommended POI locations is an interesting subject itself. Despite its importance, existing POI recommender studies generally focus on and evaluate prediction accuracy. In this article, geographical diversification ( geo-diversification ), a novel diversification concept that aims to increase recommendation coverage for a target users’ geographic areas of interest, is introduced, from which a method that improves geo-diversity as an addition to existing state-of-the-art POI recommenders is proposed. In experiments with the datasets from two real Location Based Social Networks (LSBNs), we first analyze the performance of four state-of-the-art POI recommenders from various evaluation perspectives including category diversity and geo-diversity that have not been examined previously. The proposed method consistently improves geo-diversity (CPR(geo)@20) by 5 to 12% when combined with four state-of-the-art POI recommenders with negligible prediction accuracy (Recall@20) loss and provides 6 to 18% geo-diversity improvement with tolerable prediction accuracy loss (up to 2.4%).

A Multi-Element Hybrid Location Recommendation Algorithm for Location Based Social Networks

In the environment of data explosion, how to make an effective and accurate personalized point of interest (POI) recommendation in location-based social networks (LBSNs) is a challenging and meaningful task. Fortunately, there is a lot of information that we can use. We can make recommendations by mining the rich information hidden in user check-in records. In this paper, we propose a recommend system named GFP-LORE. Specifically, we have designed a framework, which integrates various influencing factors. First, we modeled friend sign-in frequencies and POI popularity as a power-law distribution and the experiment proved that it is effective. Then, we got the influence of geographic information by modeling it according to the longitude and latitude of the user’s check-in location. After that, we sorted the historical check-in records of all users according to time and then mine an overall pattern of location transfer-order pattern. Then, we combine it with each user’s own unique location transfer record to get the possibility of the user going to the next POI. Finally, we synthesize the above four influence factors into a unified correlation probability rating and recommend a new location by this probability rating. We tested our system on the open real check-in data set, and the results of our simulation experiments show that the recommendation effect of our system is better than those algorithms used in the contrast test.

Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation

In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding environment and greatly benefits the business in real life. Although a personalized POI recommendation system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model human mobility and the difficulty to address data sparsity. Moreover, geographical influence on users should be personalized, but current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we design a novel and effective personalized POI recommendation system. First, our system mines the target user's active area based on his or her check-in history, and designs a personalized user spatial similarity calculation method based on the target user's active area. Secondly, our system takes into account three features of the human mobility pattern: spatial, temporal, and sequential properties. Furthermore, our system designs a novel personalized user mobility pattern similarity calculation method based on the features of human mobility pattern. Finally, a recommendation list is generated based on the idea of collaborative filtering. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance.

Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation

Personalized point-of-interest (POI) recommendation is important to location-based social networks (LBSNs) for helping users to explore new places and for helping third-party services to launch targeted advertisements. Discovering effective features or representations from check-in data is the key to POI recommendation. Deep learning is a representation-learning method with multiple levels for discovering intrinsic features to better represent user preferences. We analyzed users’ check-in behavior in detail and developed a deep learning model to integrate geographical and social influences for POI recommendation tasks. We used a semi-restricted Boltzmann machine to model the geographical similarity and a conditional layer to model the social influence. Experiments with real-world LBSNs showed that our method performed better than other state-of-the-art methods. Theoretically, our study contributes to the effective usage of data science and analytics for social recommender system design. In practice, our results can be used to improve the quality of personalized POI recommendation services for websites and applications.

A Common Topic Transfer Learning Model for Crossing City POI Recommendations.

With the popularity of location-aware devices (e.g., smart phones), large amounts of location-based social media data (i.e., user check-in data) are generated, which stimulate plenty of works on personalized point of interest (POI) recommendations using machine learning techniques. However, most of the existing works could not recommend POIs in a new city to a user where the user and his/her friends have never visited before. In this paper, we propose a common topic transfer learning graphical model-the common-topic transfer learning model (CTLM)-for crossing-city POI recommendations. The proposed model separates the city-specific topics (or features) of each city from the common topics (or features) shared by all cities, to enable the users' real interests in the source city to be transferred to the target city. By doing so, the ill-matching problem between users and POIs from different cities can be well addressed by preventing the real interests of users from being influenced by the city-specific features. Furthermore, we incorporate the spatial influence into our proposed model by introducing the regions' accessibility. As a result, the co-occurrence patterns of users and POIs are modeled as the aggregated result from these factors. To evaluate the performance of the CTLM, we conduct extensive experiments on Foursquare and Twitter datasets, and the experimental results show the advantages of CTLM over the state-of-the-art methods for the crossing-city POI recommendations.

Exploiting Spatial and Temporal for Point of Interest Recommendation

An increasing number of users have been attracted by location-based social networks (LBSNs) in recent years. Meanwhile, user-generated content in online LBSNs like spatial, temporal, and social information provides an ever-increasing chance to study the human behavior movement from their spatiotemporal mobility patterns and spawns a large number of location-based applications. For instance, one of such applications is to produce personalized point of interest (POI) recommendations that users are interested in. Different from traditional recommendation methods, the recommendations in LBSNs come with two vital dimensions, namely, geographical and temporal. However, previously proposed methods do not adequately explore geographical influence and temporal influence. Therefore, fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains potential. In this work, our aim is to generate a top recommendation list of POIs for a target user. Specially, we explore how to produce the POI recommendation by leveraging spatiotemporal information. In order to exploit both geographical and temporal influences, we first design a probabilistic method to initially detect users’ spatial orientation by analyzing visibility weights of POIs which are visited by them. Second, we perform collaborative filtering by detecting users’ temporal preferences. At last, for making the POI recommendation, we combine the aforementioned two approaches, that is, integrating the spatial and temporal influences, to construct a unified framework. Our experimental results on two real-world datasets indicate that our proposed method outperforms the current state-of-the-art POI recommendation approaches.

Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further insights into user preferences and behaviors. In this article, we aim to exploit geotagged photos from online photo-sharing sites for the purpose of personalized Point-of-Interest (POI) recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users’ tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We also build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences.

A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization

Recently, location-based services (LBSs) have been increasingly popular for people to experience new possibilities, for example, personalized point-of-interest (POI) recommendations that leverage on the overlapping of user trajectories to recommend POI collaboratively. POI recommendation is yet challenging as it suffers from the problems known for the conventional recommendation tasks such as data sparsity and cold start, and to a much greater extent. In the literature, most of the related works apply collaborate filtering to POI recommendation while overlooking the personalized time-variant human behavioral tendency. In this article, we put forward a fourth-order tensor factorization-based ranking methodology to recommend users their interested locations by considering their time-varying behavioral trends while capturing their long-term preferences and short-term preferences simultaneously. We also propose to categorize the locations to alleviate data sparsity and cold-start issues, and accordingly new POIs that users have not visited can thus be bubbled up during the category ranking process. The tensor factorization is carefully studied to prune the irrelevant factors to the ranking results to achieve efficient POI recommendations. The experimental results validate the efficacy of our proposed mechanism, which outperforms the state-of-the-art approaches significantly.