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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]

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]