Point-of-interest Recommendation Framework Research Articles

A Multi-channel Next POI Recommendation Framework with Multi-granularity Check-in Signals

Current study on next point-of-interest (POI) recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-grained signals (i.e., region- and global-level check-ins) in such sparse check-ins would also benefit to augment user preference learning. Specifically, our data analysis unveils that user movement exhibits noticeable patterns w.r.t. the regions of visited POIs. Meanwhile, the global all-user check-ins can help reflect sequential regularities shared by the crowd. We are, therefore, inspired to propose the MCMG: a Multi-Channel next POI recommendation framework with Multi-Granularity signals categorized from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level. The MCMG is equipped with three modules, namely, global user behavior encoder, local multi-channel (i.e., region, category, and POI channels) encoder, and region-aware weighting strategy. Such design enables MCMG to be capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the check-in patterns w.r.t. visited regions. Extensive experiments on four real-world datasets show that our MCMG significantly outperforms state-of-the-art next POI recommendation 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.

A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System

Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets.

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.

Successive Point-of-Interest Recommendation With Personalized Local Differential Privacy

The fast development of vehicle positioning technologies has paved the way for in-vehicle recommendation systems. Successive Point-of-Interest (POI) recommendation, one of the most common forms of in-vehicle recommendation, can help users choose places where they might visit in the future. Generally, to recommend POIs for users, raw check-in data are collected from in-vehicle devices, which raises users’ privacy concerns. To address this problem, we propose a successive POI recommendation framework with local differential privacy. The proposed framework exploits three types of influence factors for recommendation, including geographical influence, temporal influence, and transition pattern between two POIs. To protect users’ check-in data, we propose a distance-weighted location perturbation mechanism that realizes local differential privacy. Considering that different users may have different privacy requirements, we enhance this perturbation mechanism to provide personalized privacy protection. We use the EM reconstruction method to correct the error caused by perturbation on the server side. To improve the accuracy, we design an individual-weighted EM reconstruction method. Simulation results demonstrate that the proposed methods can provide strong privacy protection for users without hurting the recommendation accuracy too much.

Leveraging graph neural networks for point-of-interest recommendations

Point-of-Interest (POI) recommendation, i.e., suggesting POIs that a user is likely to visit, is a key task to improve user experience in location based social networks (LBSNs). Existing models either focus on geographical influence without considering other factors such as social influence and temporal influence or rely on linear methods to combine different modeling factors, lacking a sophisticated and systematical way to learn representations for users and POIs for recommendation. To remedy these issues, in this work we propose GNN-POI, a generic POI recommendation framework that leverages Graph Neural Networks (GNNs), which demonstrate powerful modeling capacity to learn node representations from node information and topological structure to improve POI recommendation. Specifically, we construct a LBSN graph comprising of two types of nodes, i.e., user node and POI node. For a target user, her preference representation is learned by combining (1) representations of her social connection nodes and (2) representations of the visited POI nodes. For social connection nodes integration, in order to model the complicated and multifaceted social influence, an attention mechanism is applied to learn strengths of heterogeneous social relations; for location nodes integration, we utilize Bi-directional Long Short-Term Memory (Bi-LSTM) to model users’ sequential check-in behavior, taking into account geographical and temporal features. Extensive experiments conducted over three real LBSN datasets show that the proposed GNN based framework significantly outperforms the state-of-the-art POI recommendation models in terms of precision, recall and Normalized Discounted Cumulative Gain (NDCG).

Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods.

Successive Point-of-Interest Recommendation With Local Differential Privacy

A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The existing POI recommendation systems require raw check-in history of users, which might cause location privacy violations. Although there have been several matrix factorization (MF) based privacy-preserving recommendation systems, they can only focus on user-POI relationships without considering the human movements in check-in history. To tackle this problem, we design a successive POI recommendation framework with local differential privacy, named SPIREL. SPIREL uses two types of information derived from the check-in history as input for the factorization: a transition pattern between two POIs and the visit counts of POIs. We propose a novel objective function for learning the user-POI and POI-POI relationships simultaneously. We further integrate local differential privacy mechanisms in our proposed framework to prevent potential location privacy breaches. Experiments using four public datasets demonstrate that SPIREL achieves better POI recommendation quality while accomplishing stronger privacy protection.

Practical Privacy Preserving POI Recommendation

Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the basis of collecting users’ data. Both private data and models are held by the recommender, which causes serious privacy concerns. In this article, we propose a novel Privacy preserving POI Recommendation (PriRec) framework. First, to protect data privacy, users’ private data (features and actions) are kept on their own side, e.g., Cellphone or Pad. Meanwhile, the public data that need to be accessed by all the users are kept by the recommender to reduce the storage costs of users’ devices. Those public data include: (1) static data only related to the status of POI, such as POI categories, and (2) dynamic data dependent on user-POI actions such as visited counts. The dynamic data could be sensitive, and we develop local differential privacy techniques to release such data to the public with privacy guarantees. Second, PriRec follows the representations of Factorization Machine (FM) that consists of a linear model and the feature interaction model. To protect the model privacy, the linear models are saved on the users’ side, and we propose a secure decentralized gradient descent protocol for users to learn it collaboratively. The feature interaction model is kept by the recommender since there is no privacy risk, and we adopt a secure aggregation strategy in a federated learning paradigm to learn it. To this end, PriRec keeps users’ private raw data and models in users’ own hands, and protects user privacy to a large extent. We apply PriRec in real-world datasets, and comprehensive experiments demonstrate that, compared with FM, PriRec achieves comparable or even better recommendation accuracy.

Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks

Point-of-Interest (POI) recommendation is one of the most essential tasks in LBSNs to help users discover new interesting locations, especially when users travel out of town or to unfamiliar areas. Current studies on POI recommendation in LBSNs mainly focus on modeling multiple factors extracted from users’ profiles and checking-in records. Data sparsity and incompleteness of user-POI interaction matrix are very common problems in POI recommendation, especially for the out-of-town scenario. Another challenge is that most information in the LBSNs is unreliable due to users’ different backgrounds or preferences. Because of the close relationship between users, information from trustable friends on Communication-Based Social Networks (CBSNs) is more valuable than that in LBSNs, which can give a preferable suggestion instead of trustless reviews in LBSNs. In this study, we propose a latent probabilistic generative model called HI-LDA (Heterogeneous Information based LDA), which can accurately capture users’ words on CBSNs by taking into full consideration the information on LBSNs including geographical effect as well as the abundant information including social relationship, users’ interactive behaviors and comment content. In particular, the parameters of the HI-LDA model can be inferred by the Gibbs sampling method in an effective fashion. Beyond these proposed techniques, we introduce an POI recommendation framework integrating geographical clustering approach considering the locations and popularity of POIs simultaneously. Extensive experiments were conducted to evaluate the performance of the proposed framework on two real heterogeneous LBSN-CBSN networks. The experimental results demonstrate the superiority of HI-LDA on effective and efficient POI recommendation in both home-town and out-of-town scenarios, when compared with the state-of-the-art baseline approaches.

Content-aware point-of-interest recommendation based on convolutional neural network

Point-of-interest (POI) recommendation has become an important approach to help people discover attractive locations. But the extreme sparsity of the user-POI matrix creates a severe challenge. To address this challenge, researchers have begun to explore the review content information for POI recommendations. Existing methods are based on bag-of-words or embedding techniques which leads to a shallow understanding of user preference. In order to capture valuable information about user preference, we propose a content-aware POI recommendation based on convolutional neural network (CPC). We utilize a convolutional neural network as the foundation of a unified POI recommendation framework and introduce the three types of content information, including POI properties, user interests and sentiment indications. The experimental results indicate that convolutional neural network is very capable of capturing semantic and sentiment information from review content and demonstrate that the relevant information in reviews can improve POI recommendation performance on location-based social networks.

Points-of-interest recommendation based on convolution matrix factorization

A point-of-interest(POI) recommendation aims to mine a user’s visiting history and find her/his potentially preferred places. The decision process when choosing a POI is complex and can be influenced by numerous factors, including personal preferences, geographical considerations, and user social relations. While latent factor models have been proven effective and are widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of location-based social networks (LBSNs). To this end, in this paper, we propose a joint convolution matrix factorization model, named the Review Geographical Social (ReGS) which strategically takes various factors into consideration. Specifically, this model captures geographical influences from a user’s check-in behaviour, and user social relations can be effectively leveraged in the recommendation model. The reviews information available on LBSNs could be related to a user’s check-in action, providing a unique opportunity for a POI recommendation. We model above three types of information under a unified POI recommendation framework based on convolution matrix factorization which integrates a convolutional neural network into a probability matrix factorization. Finally, we conduct a comprehensive performance evaluation for the ReGS using two real-world datasets collected from Foursquare. Experimental results show that the ReGS achieves significantly superior precision and recall rates to other state-of-the-art recommendation models.

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.