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Point-of-Interest (POI) recommender systems can effectively assist users to find their preferred POIs. Recent studies mainly focus on extracting users' dynamic context from their check-in behaviors and using attention mechanism to capture different influence of context information for predicting their real-time requirements. However, the existing methods mainly focus on learning the weights of different POI as well as their correlations in the check-in sequences. In addition, these methods still suffer from limited performance, especially when the interaction data are sparse. In this paper, we propose a C ontext- and C ategory-aware D ouble S elf-A ttention (CCDSA) model for POI recommendation to explore and capture users' contextual preferences in two different aspects collaboratively, including the fine-grained preference for POI in check-in behaviors and the coarse-grained preference for category. Specifically, we first design a double self-attention mechanism module to learn the users' preferences for both POI and category in specific context. Then we combine users' check-in behaviors with POIs' category information to alleviate data sparsity problem in context-aware recommendation. Finally, we leverage context and category information to perform personalized POI recommendation. In particular, we devise an improved version of CCDSA, i.e., CCDSA+, which further replaces the self-attention mechanism with the sparse self-attention mechanism for improving training efficiency. The experimental results on four real-world datasets, Foursquare-NY, Foursquare-TKY, Weeplaces-NY and Weeplaces-SF show that the proposed models, CCDSA and CCDSA+, outperform the baselines. [ABSTRACT FROM AUTHOR]

Nowadays, many people like to share the places they visited in the Location-based Social Networks (LBSNs). A Point of Interest (POI) recommendation, as one of the location-based services, helps users find new locations they prefer to visit. Recently, researchers have proposed many methods to leverage user-generated content, such as check-ins, for POI recommendation. However, due to the sparsity of user check-in information, it is still very difficult to recommend appropriate and accurate locations to users. To address the problem, in this paper, we propose a novel POI recommendation method named NGPR. Firstly, we construct a heterogeneous LBSN graph of users, POIs, categories and time periods. based on check-in records. Subsequently, the Node2Vec technique is employed to establish the latent vectors of POIs and users. Finally, we integrate comprehensive factors including the category preference, geographical distance and POI popularity for POI recommendation. The NGPR method is applied to two real LBSN datasets for experimental analysis. The experimental results show that the precision@5 of our method achieves 18.82% and 19.19% higher than that of the second best method on two real LBSN datasets respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of Information Technology, there exist massive amounts of data available on the Internet, which result in a severe information overload problem. Especially, it becomes more and more challenging but necessary to help users find the contents or services that they really need. To address the problem mentioned above, recommender systems have been developed to exploit user's historical behavior data and provide personalized services for promoting customer experiences in many fields, such as Point of Interest (POI) applications, multimedia services, and e-commerce websites. Specifically, in POI recommendation, user's next check-in behaviors depend on both long- and short-term preferences. However, traditional recommendation methods often ignore the dynamic changes of user's short-term preferences over time, which limits their performance. Besides, many existing methods cannot fully exploit the complex correlations and transitions between POI in check-ins sequences. In this paper, we propose an A ttentive S equential model based on G raph N eural N etwork (ASGNN) for accurate next POI recommendation. Specifically, ASGNN firstly models user's check-in sequences as graphs and then use Graph Neural Networks (GNN) to learn the informative low-dimension latent feature vectors (embeddings) of POIs. Secondly, a personalized hierarchical attention network is adopted to exploit complex correlations between users and POIs in check-in sequences and capture user's long- and short-term preferences. Finally, we perform the next POI recommendation via leveraging user's long- and short-term preferences obtained from their behavior sequences with ASGNN. Extensive experiments are conducted on three real-world check-in datasets, and the results demonstrate that the proposed model ASGNN outperforms baselines, including some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Next Point-of-Interest (POI) recommendation is highly challenging in its ill-posedness of data sparsity and elusive motives. Many models, including sequence- and graph-based, have been proposed to alleviate these problems. However, they still contain drawbacks: they either need a more accurate depiction of users' complex trajectories or novel perspectives on temporal-variant features in the discretized sequences of visits. To better cope with the two challenges, we proposed a novel time-continuous model, namely POIGDE. Our model explicitly exploits continuous variation of users' interests by solving a graph differential equation (GDE) on users' interaction behaviors. To maintain an invariant distribution while solving GDEs, we utilize a time-serial graph along with an interval-aware attention mechanism to learn the dynamics of interest transference. This novel update mechanism helps to track the original data distribution by confining the update process in a linear combination manner. Under this restriction, our model can obtain item representations for prediction, which share the same distribution as the actual POIs with which users interact. This practice also applies Siamese Learning in the POI recommendation to directly learn from the representation of ground-truth labels. By comparison with positive samples, the inferred probability of POIs being visited in the future can be obtained based on similarity (i.e., mean square error) between high-dimensional representations of the corresponding POIs. Our model outperforms state-of-the-art models on three real-world datasets by 0.16-23.44%, showing its potential and prospects in the POI recommendation domain. • Propose Graph Differential Equation to model the continuity of users' interest. • Combine sequence- and graph-based models to obtain a time-serial graph. • Design Siamese learning-based strategy to alleviate the bias on negative samples. [ABSTRACT FROM AUTHOR]

The effective Point-of-Interest (POI) recommendation can significantly assist users to find their preferred POIs and help POI owners to attract more customers. As a result, a variety of methods have been proposed to tackle the issue of POI recommendation recently. However, it is still very difficult to precisely model the strong correlations between the POIs visited by the user and the POIs to be visited next, which leads to the poor performance of POI recommendation. In this paper, we propose a context- and preference- aware model (CPAM) to incorporate both contextual influence and user preferences into POI recommendation. Firstly, we design a Skip-Gram based POI Embedding Model (SG-PEM) to capture the contextual influence of POIs and learn the vector representation (embedding) of POIs from visiting sequences. The users' preferences for the target POIs are obtained from the learned embeddings via similarity metric. Secondly, for the implicit feedback information contained in the check-in data, we use the Logistic Matrix Factorization (LMF) algorithm to model the users' personalized preferences for POI. Finally, we unify SG-PEM and LMF as the CPAM model to perform personalized recommendation by leveraging contextual influence and user preferences. The experimental results on two real-world datasets of Foursquare and Gowalla show that the proposed model outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recently, deep neural networks are widely applied in recommender systems for their effectiveness in capturing/modeling users' preferences. Especially, the attention mechanism in deep learning enables recommender systems to incorporate various features in an adaptive way. Specifically, as for the next item recommendation task, we have the following three observations: 1) users' sequential behavior records aggregate at time positions ("time-aggregation"), 2) users have personalized taste that is related to the "time-aggregation" phenomenon ("personalized time-aggregation"), and 3) users' short-term interests play an important role in the next item prediction/recommendation. In this paper, we propose a new T ime-aware L ong- and S hort-term A ttention N etwork (TLSAN) to address those observations mentioned above. Specifically, TLSAN consists of two main components. Firstly, TLSAN models "personalized time-aggregation" and learn user-specific temporal taste via trainable personalized time position embeddings with category-aware correlations in long-term behaviors. Secondly, long- and short-term feature-wise attention layers are proposed to effectively capture users' long- and short-term preferences for accurate recommendation. Especially, the attention mechanism enables TLSAN to utilize users' preferences in an adaptive way, and its usage in long- and short-term layers enhances TLSAN's ability of dealing with sparse interaction data. Extensive experiments are conducted on Amazon datasets from different fields (also with different size), and the results show that TLSAN outperforms state-of-the-art baselines in both capturing users' preferences and performing time-sensitive next-item recommendation. [ABSTRACT FROM AUTHOR]

Point of Interest (POI) recommendation algorithms can help users find the POIs that they prefer, and they can also help merchants to find potential customers. However, most existing methods still have difficulties effectively utilizing the information in users' check-in data. Significantly, they ignore the intent behind the users' check-in behaviors, which limits the recommendation performance. In this paper, we propose an I ntent A ware G raph N eural N etwork-based model(IAGNN) to predict/recommend the next POI with which the target user may interact. Specifically, IAGNN first models the user's check-in behavior sequences as graphs and utilizes the information transmission mechanism of the graph neural network (GNN) to learn the feature vector representation (embedding) of POIs. Second, we devise a hierarchical attention network for capturing users' preferences adaptively. At the same time, we design a user intent-aware module based on disentangled representations to extract the user's intents. Finally, the user's preferences and their intents obtained by the user intent perception module are combined to recommend the POI for the user. Extensive evaluations are conducted on two real-world POI check-in datasets. The experimental results show that our proposed model IAGNN outperforms the baselines in terms of both recall and MRR. [ABSTRACT FROM AUTHOR]

Next point-of-interest (POI) recommendation has emerged as an essential task in recommender systems with the rapid development of location-based social networks (LBSNs). It has a wide range of applications in smart cities for building personalized scenarios. Current research typically uses sequential relationships to mine user preferences; however, it fails to sufficiently explore the spatial dependence of check-ins and the multi-perspective information they contain. To this end, this study proposes a Multiple Active Region Aware Network (MARAN), a novel routine-aware model for the next POI recommendation that simultaneously captures the user's routine regularity and short-term preference changes from check-in records. The key to MARAN is its ability to decompose sophisticated user behavior into two parts. One is a stable routine part characterized by central-based graphs built from historical trajectories based on spatial aggregation. The other is an unstable preference part that obtains the user's recent changes from short-term trajectories. Moreover, a neighborhood-aware negative sampler based on adjacent areas was designed to alleviate spatial sparsity, that is, the imbalance between positive and negative samples during model training. Experiments on two real-world datasets demonstrated that MARAN outperformed state-of-the-art methods. • Next POI recommendation considering spatial aggregation. • User's routines are characterized by central-based graphs. • Users' recent preferences are obtained from short-term trajectories. • Dynamic negative sampling based on current position. [ABSTRACT FROM AUTHOR]