Journal articles on the topic 'Next POI Recommendation'

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.

As the core of location-based social networks (LBSNs), the main task of next point-of-interest (POI) recommendation is to predict the next possible POI through the context information from users’ historical check-in trajectories. It is well known that spatial–temporal contextual information plays an important role in analyzing users check-in behaviors. Moreover, the information between POIs provides a non-trivial correlation for modeling users visiting preferences. Unfortunately, the impact of such correlation information and the spatio–temporal unequal interval information between POIs on user selection of next POI, is rarely considered. Therefore, we propose a spatio-temporal unequal interval correlation-aware self-attention network (STUIC-SAN) model for next POI recommendation. Specifically, we first use the linear regression method to obtain the spatio-temporal unequal interval correlation between any two POIs from users’ check-in sequences. Sequentially, we design a spatio-temporal unequal interval correlation-aware self-attention mechanism, which is able to comprehensively capture users’ personalized spatio-temporal unequal interval correlation preferences by incorporating multiple factors, including POIs information, spatio-temporal unequal interval correlation information between POIs, and the absolute positional information of corresponding POIs. On this basis, we perform next POI recommendation. Finally, we conduct comprehensive performance evaluation using large-scale real-world datasets from two popular location-based social networks, namely, Foursquare and Gowalla. Experimental results on two datasets indicate that the proposed STUIC-SAN outperformed the state-of-the-art next POI recommendation approaches regarding two commonly used evaluation metrics.

The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN.

Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.

Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.

Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.

With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average.

Recommending and planning tour itineraries are challenging and time-consuming for tourists, hence they may seek tour operators for help. Traditionally tour operators have offered standard tour packages of popular locations, but these packages may not cater to tourist's interests. In addition, tourists may want to travel in a group, e.g., extended family, and want an operator to help them. We introduce the novel problem of group tour recommendation (GroupTourRec), which involves many challenges: forming tour groups whose members have similar interests; recommending Points-of-Interests (POI) that form the tour itinerary and cater for the group's interests; and assigning guides to lead these tours. For each challenge, we propose solutions involving: clustering for tourist groupings; optimizing a variant of the Orienteering problem for POI recommendations; and integer programming for tour guide assignments. Using a Flickr dataset of seven cities, we compare our proposed approaches against various baselines and observe significant improvements in terms of interest similarity, total/maximum/minimum tour interests and total tour guide expertise.

PurposeTourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.Design/methodology/approachThe authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.FindingsThe authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.Originality/valueThis study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

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.

AbstractRecommending a limited number of Point-of-Interests (POIs) a user will visit next has become increasingly important to both users and POI holders for Location-Based Social Networks (LBSNs). However, POI recommendation is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recent studies show that embedding techniques effectively incorporate POI contextual information to alleviate the data sparsity issue, and Recurrent Neural Network (RNN) has been successfully employed for sequential prediction. Nevertheless, existing POI recommendation approaches are still limited in capturing user personalized preference due to separate embedding learning or network modeling. To this end, we propose a novel unified spatio-temporal neural network framework, named PPR, which leverages users’ check-in records and social ties to recommend personalized POIs for querying users by joint embedding and sequential modeling. Specifically, PPR first learns user and POI representations by joint modeling User-POI relation, sequential patterns, geographical influence, and social ties in a heterogeneous graph and then models user personalized sequential patterns using the designed spatio-temporal neural network based on LSTM model for the personalized POI recommendation. Furthermore, we extend PPR to an end-to-end recommendation model by jointly learning node representations and modeling user personalized sequential preference. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms state-of-the-art baselines for successive POI recommendation in terms of Accuracy, Precision, Recall and NDCG. The source code is available at: https://www.anonymous.4open.science/r/DSE-1BEC.

With the widespread of location-based social networks (LBSNs), the amount of check-in data grows rapidly, which helps to recommend the next point-of-interest (POI). Extracting sequential patterns from check-in data has become a meaningful way for next POI recommendation, since human movement exhibits sequential patterns in LBSNs. However, due to the check-ins’ sparsity problem, exploiting sequential patterns in next POI recommendation is a challenging issue, which makes the learned sequential patterns unreliable. Inspired by the fact that auxiliary information can be incorporated to alleviate this situation, in this paper, we model sequential transition based on both item-wise check-in sequences and region-wise spatial information. Besides, we propose an attention-aware recurrent neural network (ATTRNN) to learn the contribution of different time steps. Furthermore, considering users’ decision-making is influenced by public’s common preference to some extent, we design a novel framework, namely HSP (short for “Hybrid model based on Sequential feature mining and Public preference awareness”), to recommend POIs for a given user. We conduct a comprehensive performance evaluation for HSP on two real-world datasets. Experimental results demonstrate that compared to other state-of-the-art techniques, the proposed HSP achieves significantly improvements.

In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches.

The next point-of-interest (POI) recommendation is one of the most essential applications in location-based social networks (LBSNs). Its main goal is to research the sequential patterns of user check-in activities and then predict a user’s next destination. However, most previous studies have failed to make full use of spatio-temporal information to analyze user check-in periodic regularity, and some studies omit the user’s transition preference for the category at the POI semantic level. These are important for analyzing the user’s preference for check-in behavior. Long- and short-term preference modeling based on multi-level attention (LSMA) is put forward to solve the above problem and enhance the accuracy of the next POI recommendation. This can capture the user’s long-term and short-term preferences separately, and consider the multi-faceted utilization of spatio-temporal information. In particular, it can analyze the periodic hobbies contained in the user’s check-in. Moreover, a multi-level attention mechanism is designed to study the multi-factor dynamic representation of user check-in behavior and non-linear dependence between user check-ins, which can multi-angle and comprehensively explore a user’s check-in interest. We also study the user’s category transition preference at a coarse-grained semantic level to help construct the user’s long-term and short-term preferences. Finally, experiments were carried out on two real-world datasets; the findings showed that LSMA modeling outperformed state-of-the-art recommendation systems.

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

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.

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.

Abstract The advances of mobile equipment and localization techniques put forward the accuracy of the location-based service (LBS) in mobile networks. One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate point-of-interest (POI) recommendation based on users’ interests. Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users’ attributes or knowing the detailed features of the vast number of POIs. To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of the collaborative, sequential and content-aware information. Unlike previous strategies that equally treat POIs in the sequence or manually define the relationships between POIs, GARG adaptively differentiates the relevance of POIs in the sequence to the prediction, and automatically identifies the POI-wise correlation. Extensive experiments on three real-world datasets demonstrate the effectiveness of GARG and reveal a significant improvement by GARG on the precision, recall and mAP metrics, compared to several state-of-the-art baseline methods.

With the increasing popularity of location-aware Internet-of-Vehicle services, the next-Point-of-Interest (POI) recommendation has gained significant research interest, predicting where drivers will go next from their sequential movements. Many researchers have focused on this problem and proposed solutions. Machine learning-based methods (matrix factorization, Markov chain, and factorizing personalized Markov chain) focus on a POI sequential transition. However, they do not recommend the user’s position for the next few hours. Neural network-based methods can model user mobility behavior by learning the representations of the sequence data in the high-dimensional space. However, they just consider the influence from the spatiotemporal dimension and ignore many important influences, such as duration time at a POI (Point of Interest) and the semantic tags of the POIs. In this paper, we propose a novel method called multi-dimension spatial–temporal recurrent neural networks (MST-RNN), which extends the ST-RNN and exploits the duration time dimension and semantic tag dimension of POIs in each layer of neural networks. Experiments on real-world vehicle movement data show that the proposed MST-RNN is effective and clearly outperforms the state-of-the-art methods.

<p><strong>Abstract.</strong> Providing mobile map users with relevant information about their surroundings based on their current trajectory is a necessary next step in providing them with the information they need or want without requiring direct interaction with the map, which can be dangerous or distracting, as well as time-consuming and annoying. Providing these recommendations requires integrating spatial information from the mobile device’s GPS chip with attributes about the underlying map and point of interest (POI) data, as well as the preferences and goals of the user. The Flyover Country app provides a relatively contained test case for the development of predictive software for recommending current and upcoming POIs during travel.</p>

♦Introduction The month of Ramadan holds great religious and social significance for Muslims all over the world. The aim of the present study was to provide a modified dialysis schedule for peritoneal dialysis (PD) patients that allows for fasting and that minimizes the effect on the patient's general health and volume status. ♦Methods We observed 31 patients under treatment at the PD unit of King Khalid University Hospital, King Saud University, Riyadh. During the 3 - 4 weeks before the start of Ramadan, all patients were counseled individually and in detail about the possibility of fasting. They were also provided with clear instructions about fluid intake (up to 1 L daily) and avoiding a high-potassium diet. Of the 31 patients, 18 (10 women, 8 men) elected to fast during the month of Ramadan. The mean duration of fast in the study year (2009) in Riyadh, Saudi Arabia, was about 14 hours: from 0415 h (before sunrise) to 1800 h (after sunset). Depending on membrane type and patient preference, the fasting group was shifted to one of two regimens: • Modified continuous ambulatory PD (8 patients): 3 exchanges during the night (1.36% or 2.27%), and icodextrin for a long dwell during the day. The first dialysis exchange was performed immediately after breaking the fast (1900 h), and the next at 2300 h. The final exchange was performed in the early morning before sunrise (0300 h), when the icodextrin was infused. • Modified continuous cycling PD (10 patients): exchanges (1.36% or 2.27%) were performed over 6 - 7 hours, and icodextrin was infused for a long dwell during the day. The patient connected to the cycler at 2000 h or 2100 h, and therapy finished at nearly 0300 h, with icodextrin as the last fill. ♦Results Of the study patients, 2 were admitted because of peritonitis (1 in each modality group), and the modified therapy was discontinued. In the modified CCPD group, 1 patient (on PD for 1 month before Ramadan) developed PD-related pleural effusion (proved by pleural fluid analysis), and PD was consequently discontinued. Hypotension developed in 2 patients of the CAPD group and 1 of the CCPD group during the first 2 weeks. In the CCPD group, 1 patient presented with lower limb edema and mild fluid overload. Overall, PD patients that opted to fast during Ramadan did not experience any serious morbidity or deterioration in renal function during their period of observance. No biochemical parameters or clearance studies showed a statistically significant p value. ♦Conclusions In view of the study findings, we conclude that most stable patients on PD can fast, provided that they strictly adhere to their medications and dialysis therapy in addition to the dietary restrictions. These patients should be followed closely to detect any complications and to ensure that adequate fluid and electrolyte balance are maintained.

AbstractRecommendations by the National Research Council (NRC), the National Institute of Standards and Technology (NIST), and Weather-Ready Nation workshop participants have encouraged the National Oceanic and Atmospheric Administration (NOAA) and the broader weather enterprise to explore and expand the use of probabilistic information to convey weather forecast uncertainty. Forecasting a Continuum of Environmental Threats (FACETs) is a concept being explored by NOAA to address those recommendations and also potentially shift the National Weather Service (NWS) from (primarily) teletype-era, deterministic watch–warning products to high-resolution, probabilistic hazard information (PHI) spanning periods from days (and longer) to within minutes of high-impact weather and water events. FACETs simultaneously i) considers a reinvention of the NWS hazard forecasting and communication paradigm so as to deliver multiscale, user-specific probabilistic guidance from numerical weather prediction ensembles and ii) provides a comprehensive framework to organize the physical, social, and behavioral sciences, the technology, and the practices needed to achieve that reinvention. The first applications of FACETs have focused on thunderstorm phenomena, but the FACETs concept is envisioned to extend to the attributes of any environmental hazards that can be described probabilistically (e.g., winter, tropical, and aviation weather). This paper introduces the FACETs vision, the motivation for its creation, the research and development under way to explore that vision, its relevance to operational forecasting and society, and possible strategies for implementation.

Purpose: Despite recent advances in interventional cardiology procedures, isolated ostial left anterior descending (LAD) lesions remain a challenge in cardiology. Due to the probability of left main (LM) affection, an LM bifurcation stenting technique may be required in certain individuals. So we evaluated result of Intravascular Ultrasound (IVUS) guided percutaneous coronary intervention (PCI) in isolated LAD coronary artery lesions, either by crossing over the ostium or not, with a focus on major adverse cardiovascular events (MACE). Methodology: Our prospective study from January2021 to November 2022 included 79 isolated ostial LAD patients with Ostial LAD stenting (OS) or LM to LAD cross-over (CO) stenting at the National Heart Institute. As per the recommended guidelines, the participants were divided into two groups: the first one had IVUS guided PCI and Group (2) had Angiography guided PCI. The data was collected and statistically analyzed with SPSS 23.0 program. Findings: No statistically significant difference was present between the groups regarding socio-demographic or clinical data (P-value > 0.05). It was discovered that an increase in Rotablator use, a decrease in Fluoroscopy time (min), a decrease in Contrast volume (ml), and a decrease in PCI duration (min) in patients who had IVUS guided PCI, with statistically significant difference when compared to other group who had Angiography guided PCI (P-value < 0.05). In addition, it was discovered that an increase in the necessity for a cross-over stenting method in group (1), with no statistically significant difference (P-value > 0.05). High prevalence of mortality and morbidity among IVUS guided PCI patients was present with statistical significance regarding TVR (P-value < 0.05). Recommendations: IVUS can offer valuable information on vascular lumen, plaque features, stent deployment, & device failure causes. As a result, IVUS-guided PCI may enhance clinical impact among participants, particularly those with complicated coronary lesions & those at high risk. Further reduction in IVUS’s cost, cardiologists’ education and enhancing IVUS use at PCI should be adapted next.

Abstract Background: The FMS-like tyrosine kinase 3 (FLT3) is mutated in approximately 30% of acute myeloid leukemia (AML) patients either by internal tandem duplication (ITD) or by point mutations in the tyrosine kinase domain (TKD). PHI-101 is a Type I FLT3 inhibitor developed to overcome resistance in AML. PHI-101 showed potent cellular activity in vitro and in vivo for both FLT3 ITD mutations and a wide variety of FLT3 point mutations in TKD. In preclinical studies utilizing primary human FLT3-ITD leukemia cells, PHI-101 showed increased anti-leukemic activity compared to gilteritinib based on increased survival in mice transplanted with these samples. The Phase 1a and Phase 1b global clinical trial with PHI-101 (NCT04842370) is currently underway to assess the overall safety and efficacy of PHI-101 in refractory and relapsed AML. Study Design and Methods: The overall design of the AML clinical trial consists of two parts, phase 1a, the dose-escalation, and phase 1b, the dose-expansion trial utilizing oral PHI-101 tablets. Up to 5 dose levels are planned for phase 1a and a single subject enrolled per dose level until the occurrence of at least one subject with more than Grade 2 toxicity according to CTCAE5.0 criteria during the 28-day evaluation period. The dose escalation will then convert to a standard 3+3 scheme, with 3 to 6 subjects per dose level cohort over the 28-day cycle. If there are no dose-limiting toxicities (DLT), dose escalation to the next higher dose levels will proceed upon the recommendation of the Safety Monitoring Committee. Subjects with FLT3 mutations or FLT3 wild-type will be enrolled in the dose-escalation cohort, and blood samples are collected for the primary endpoint pharmacokinetics (PK) and for the exploratory pharmacodynamic (PD) endpoint evaluation, including plasma inhibitory assay and biomarker analysis. Results: The Phase 1a clinical trial of PHI-101 was initiated in June 2020 at a daily dose of 40mg for level 1. As of July 31, 2021, 8 patients with relapsed or refractory AML have been enrolled in the trial, all of whom had prior anti-leukemic treatments with intensive chemotherapy, hypomethylating agents and/or other FLT3 inhibitors. To date, 5 enrolled patients were available for safety assessment at three dose levels and have not experienced any dose-limiting toxicities (DLTs) from repeating doses of the 28-day DLT-assessment window. The leukemic blasts in marrow or peripheral blood were dramatically reduced by up to 98% with one cycle (28 days). The pharmacokinetic data of plasma PHI-101 varied in a dose-proportional manner and peak plasma concentrations (Cmax) were reached between 4 and 6 hours after once-daily dosing. The pharmacodynamic evaluation by plasma inhibitory assay showed that plasma from patients receiving at daily doses of PHI-101 inhibited more than 90% of the phosphorylation of the FLT3-ITD receptor. PHI-101 was well tolerated with these dose levels. This study is currently recruiting FLT3 mutation or wild-type AML patients at multiple sites in Korea and Australia. Conclusion: PHI-101 is a next-generation FLT3 inhibitor that showed a potent anti-leukemic activity and improved efficacy in primary AML samples harboring FLT3/ITD and FLT3/TKD mutations in preclinical studies. The current analysis of this trial indicated that PHI-101 is a very effective FLT3 inhibitor for both refractory and relapsed AML patients, including those that have relapsed on other FLT3 TKI. The assessments of safety, tolerability, and PK of PHI-101 to determine the recommended dose for expansion are ongoing in the Phase 1a clinical trial. Disclosures Lee: Astellas Pharma, Inc.: Consultancy, Honoraria, Other: Advisory board; AbbVie: Honoraria, Other: Advisory board; Korean Society of Hematology: Membership on an entity's Board of Directors or advisory committees. Im: Pharos iBio Co., Ltd.: Current Employment. Nam: Pharos iBio Co., Ltd.: Current holder of individual stocks in a privately-held company. Kim: Pharos iBio Co., Ltd.: Current holder of individual stocks in a privately-held company. Han: Pharos iBio Co., Ltd.: Current holder of individual stocks in a privately-held company. Yoon: Pharos iBio Co., Ltd.: Current holder of individual stocks in a privately-held company. Small: Pharos iBio Co., Ltd.: Consultancy, Other: Scientific Advisory Board. The arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict-of-interest policies, Research Funding; InSilico Medicine: Other: Scientific Advisory Board.

Background. Inappropriate cardiac catheterization lab activation together with false-positive angiographies and no-culprit found coronary interventions are now reported as costly to the medical system, influencing STEMI process efficiency. We aimed to analyze data from a high-volume interventional centre (>1000 primary PCIs/year) exploring etiologies and reporting characteristics from all “blank” coronary angiographies in STEMI. Methods. In this retrospective observational single-centre cohort study, we reported two-year data from a primary PCI registry (2035 patients). “Angio-only” cases were assigned to one of these categories: (a) Takotsubo syndrome; (b) coronary embolisation; (c) myocardial infarction with nonobstructive coronary arteries; (d) myocarditis; (e) CABG-referred; (f) normal coronary arteries (mostly diagnostic errors); and (g)others (refusals and death prior angioplasty). Univariate analysis assessed correlations between each category and cardiovascular risk factors. Results. 412 STEMI patients received coronary angiography “only,” accounting for 20.2% of cath lab activations. Barely 77 patients had diagnostic errors (3.8% from all patients) implying false-activations. 40% of “angio-only” patients (n = 165) were referred to surgery due to severe atherosclerosis or mechanical complications. Patients with diagnostic errors and normal arteries displayed strong correlations with all cardiovascular risk factors. Probably, numerous risk factors “convinced” emergency department staff to call for an angio. Conclusions. STEMI network professionals often confront with coronary angiography “only” situations. We propose a classification according to etiologies. Next, STEMI guidelines should include audit recommendations and specific thresholds regarding “angio-only” patients, with specific focus on MINOCA, CABG referrals, and diagnostic errors. These measures will have a double impact: a better management of the patient, and a clearer perception about the usefulness of the investments.

Drugs inhibiting platelet function play a major role in the treatment of acute coronary syndromes (ACS). The first drug used, which is still considered the cornerstone of therapy today, is aspirin. Although very impressive in acutely decreasing rates of myocardial infarction as well as death, long-term data are scarce, despite our current recommendation for lifelong aspirin. The thienopyridines, most notably clopidogrel, are the next line of antiplatelet drugs. Well-documented data support the usage of clopidogrel for non-STEMI-ACS (NSTE-ACS). Although positive mortality data exist regarding clopidogrel and STEMI patients in a medically treated population, including thrombolysis, no larger amounts of randomised data exist in a primary PCI setting. Poor responders to aspirin and/or clopidogrel are a clinical problem, with these individuals constituting a higherrisk group for recurrent ischaemic events. Whereas very little can be done regarding aspirin resistance, clopidogrel resistance might be diminished by increasing the dosage or changing to more potent and newer-generation antiplatelet drugs. The role of glycoprotein IIb/IIIa inhibitors has diminished drastically and instead paved the way for thrombin antagonists (bivalirudin), which have fewer bleeding complications with resulting better long-term mortality. Novel adenosine diphosphate (ADP)-receptor blockers such as prasugrel and ticagrelor have shown increased efficacy over clopidogrel and hold great promise for the future. However, not all patients may benefit from these new drugs and economic constraints may also limit their use. Platelet function tests could possibly help in identifying risk groups in need of stronger platelet inhibition.

Abstract Introduction: In the last decades, the life expectancy of patients with hemophilia A (HA), hemophilia B (HB) and von Willebrand disease (VWD) has substantially improved. As a result, these patients experience age-related comorbidities, especially ischaemic heart disease. Safety and efficacy of antiplatelet drugs in patients with inherited bleeding disorders remain unclear, while there is no evidence-based guideline for the antithrombotic management in this population. The aims of our study were to describe the management of patients with HA, HB and VWD at the occurrence of ischaemic heart disease in our regional referral center; to compare this management to experts' recommendations; and to evaluate the safety and the efficacy of antiplatelet drugs in this population. Methods : The source of population was the 2008-2018 cohort of patients with HA (n=565), HB (n=115) and VWD (n=618) followed at Toulouse University hospital (France). Their follow-up is recorded in electronic medical files. We retrospectively identified the patients who experienced an ischaemic heart disease treated by antiplatelet therapy. Ischaemic heart disease included ST- and non-ST-segment elevation acute myocardial infarction, stable and unstable angina, and silent coronary artery disease. We described the reperfusion therapy, the use of antiplatelet drugs and replacement factors, and the occurrence of bleeding or thrombotic complications during the follow-up. Results: Eight patients had an ischaemic heart disease: 5 HA, 1 HB and 2 VWD patients. Four of the haemophilic patients had minor hemophilia; the two others had moderate hemophilia. VWD patients were one type 1 (FVIII 26%, VWF:Ag 13%) and one type 2B (FVIII 29%, VWF:Ag 75%, VWF :Act 24%, low platelet count). Age at the time of the cardiac event ranged from 49 to 80 years. All patients were men except the patient with type 2B VWD. Cardiovascular risk factors were frequent (overweight, n=5; hypercholesterolemia, n=4; smoking, n=4). Four patients were investigated because of cardiac symptoms (unstable angina, angina, dyspnea, palpitations, n=1 each), and one patient because of family history. The last 3 patients were investigated as part of a screening program including patients with a high cardiovascular risk estimation. The initial management was as follows: 4 patients underwent a percutaneous coronary intervention (PCI) and 4 had a triple coronary artery bypass grafting (CABG). All patients treated with PCI had dual antiplatelet therapy for one month, then low-dose aspirin. CABG patients were initiated with low-dose aspirin. FVIII exposure was lower in PCI patients than in CABG patients (13 ± 10.42 versus 19 ± 9.35 cumulative exposure days to FVIII). Four patients were managed with differences from current guidelines1-3: first, the woman with type 2B VWD was treated with two drug-eluting stents whereas bare-metal stents are recommended. Dual antiplatelet therapy was then initiated but stopped at one month because of microcytic anemia. She was then treated with acid acetylsalicylic, 160mg per day instead of 75mg, and presented a severe gastrointestinal bleeding. Second, the patient with HB (FIX 34%) received no replacement therapy during PCI and no proton pump inhibitors while treated by antiplatelet drug, but he experienced no bleeding. Third, a HA patient (FVIII 6%) had a trough level of FVIII slightly lower than recommended (FVIII 37% versus > 50%) at day 7 after CABG. He presented a hemopericardium the next day, complicated with cardiac tamponade. Lastly, a moderate HA patient had no long-term antiplatelet therapy after CABG. However, he did not experience any new cardiovascular event during the following 4 years. During the follow-up (median: 24,5 months), only one HA (FVIII 20%) patient had a new cardiovascular event: a critical lower limb ischemia complicated with an arterial ulcer at the age of 91 years, 11 years after CABG. In contrast, 3 patients experienced a severe bleeding while treated by dual or low-dose aspirin: one hemopericardium, one gastrointestinal bleeding and one intracranial bleeding at J7 post-CABG, 13 months and 11 years after the cardiac event, respectively. Conclusion: This series of 8 patients confirms the significant risk of severe bleeding complications when antiplatelet drug is initiated in patients with hemophilia or VWD. In 1/3 cases, the severe bleeding occurred despite strict adherence to current recommendations. Disclosures No relevant conflicts of interest to declare.

A mangrove vulnerability assessment's goal is to generate recommendations for reducing vulnerability. Mangrove forests, which grow in the intertidal zones and estuary mouths between land and sea, exist in two worlds at once. Mangroves provide crucial stability for preventing shoreline erosion. It helps to maintain land level by sediment accretion while balancing sediment loss by serving as buffers catching materials washed downstream. Climate change, especially the associated increase in sea level, poses a serious threat to mangrove coastal areas, and it is critical to devise strategies to mitigate vulnerability through strategic management planning. Experts are attempting to determine how mangroves have been affected by climate change and rising sea levels. How do we forecast the consequences and effect of rising sea levels on mangroves, and then adjust and mitigate them accordingly? Vulnerability implies the risk of being assaulted or hurt, whether physically or emotionally. Environmental vulnerability is a feature of impact exposure as well as ecological systems' susceptibility and adaptive potential to environmental tensors. Researchers in this study ranked mangrove vulnerability on a scale of 1 to 5, with 1 indicating very low vulnerability and 5 indicating very high vulnerability. The Physical Mangrove Index (PMI), Biological Mangrove Index (BMI), and Threat Mangrove Index (HMI) are the three major groups of the Mangrove Vulnerability Index (MVI)). The study's main objective is to develop an accurate and efficient GIS database system that has been formulated and tested or implemented in three (3) separate areas, namely, Kukup Island, Tanjung Piai, and Sungai Pulai. The study develops a GIS-based Mangrove Vulnerability Index (MVI) Model for a selected ecosystem, and highlights mangrove vulnerability by ranking them from least to most vulnerable using parameters. The study also provides a forecast for the mangrove loss in the next 50 and 100 years, as well as to classify areas where mangroves are most vulnerable.

Climate change induced sea-level rise (SLR) is on its increase globally. Regionally the lowlands of China, Vietnam, Bangladesh, and islands of the Malaysian, Indonesian and Philippine archipelagos are among the world’s most threatened regions. Sea-level rise has major impacts on the ecosystems and society. It threatens coastal populations, economic activities, and fragile ecosystems as mangroves, coastal salt-marches and wetlands. This paper provides a summary of the current state of knowledge of sea level-rise and its effects on both human and natural ecosystems. The focus is on coastal urban areas and low lying deltas in South-East Asia and Vietnam, as one of the most threatened areas in the world. About 3 mm per year reflects the growing consensus on the average SLR worldwide. The trend speeds up during recent decades. The figures are subject to local, temporal and methodological variation. In Vietnam the average values of 3.3 mm per year during the 1993-2014 period are above the worldwide average. Although a basic conceptual understanding exists that the increasing global frequency of the strongest tropical cyclones is related with the increasing temperature and SLR, this relationship is insufficiently understood. Moreover the precise, complex environmental, economic, social, and health impacts are currently unclear. SLR, storms and changing precipitation patterns increase flood risks, in particular in urban areas. Part of the current scientific debate is on how urban agglomeration can be made more resilient to flood risks. Where originally mainly technical interventions dominated this discussion, it becomes increasingly clear that proactive special planning, flood defense, flood risk mitigation, flood preparation, and flood recovery are important, but costly instruments. Next to the main focus on SLR and its effects on resilience, the paper reviews main SLR associated impacts: Floods and inundation, salinization, shoreline change, and effects on mangroves and wetlands. The hazards of SLR related floods increase fastest in urban areas. This is related with both the increasing surface major cities are expected to occupy during the decades to come and the increasing coastal population. In particular Asia and its megacities in the southern part of the continent are increasingly at risk. The discussion points to complexity, inter-disciplinarity, and the related uncertainty, as core characteristics. An integrated combination of mitigation, adaptation and resilience measures is currently considered as the most indicated way to resist SLR today and in the near future.References Aerts J.C.J.H., Hassan A., Savenije H.H.G., Khan M.F., 2000. Using GIS tools and rapid assessment techniques for determining salt intrusion: Stream a river basin management instrument. 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Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource-intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel d ecentralized c ollaborative l earning framework for POI r ecommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models’ dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.

AbstractPoint-of-interest (POI) recommendation is a challenging problem due to different contextual information and a wide variety of human mobility patterns. Prior studies focus on recommendation that considers user travel spatiotemporal and sequential patterns behaviours. These studies do not pay attention to user personal interests, which is a significant factor for POI recommendation. Besides user interests, queuing time also plays a significant role in affecting user mobility behaviour, e.g., having to queue a long time to enter a POI might reduce visitor’s enjoyment. Recently, attention-based recurrent neural networks-based approaches show promising performance in the next POI recommendation task. However, they are limited to single head attention, which can have difficulty in finding the appropriate user mobility behaviours considering complex relationships among POI spatial distances, POI check-in time, user interests and POI queuing times. In this research work, we are the first to consider queuing time and user interest awareness factors for next POI recommendation. We demonstrate how it is non-trivial to recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi-head attention transformer model called TLR-M_UI. The model recommends the next POIs to the target users and predicts queuing time to access the POIs simultaneously by considering user mobility behaviours. The proposed model utilises POIs description-based user personal interest that can also solve the new categorical POI cold start problem. Extensive experiments on six real-world datasets show that the proposed models outperform the state-of-the-art baseline approaches in terms of precision, recall, and F1-score evaluation metrics. The model also predicts and minimizes the queuing time. For the reproducibility of the proposed model, we have publicly shared our implementation code at GitHub (https://github.com/sajalhalder/TLR-M_UI).

The task of next Point-of-Interest (POI) recommendation aims to recommend a list of POIs for a user to visit at the next timestamp based on his/her previous interactions, which is valuable for both location-based service providers and users. Recent state-of-the-art studies mainly employ recurrent neural network (RNN) based methods to model user check-in behaviors according to user’s historical check-in sequences. However, most of the existing RNN-based methods merely capture geographical influences depending on physical distance or successive relation among POIs. They are insufficient to capture the high-order complex geographical influences among POI networks, which are essential for estimating user preferences. To address this limitation, we propose a novel Graph-based Spatial Dependency modeling (GSD) module, which focuses on explicitly modeling complex geographical influences by leveraging graph embedding. GSD captures two types of geographical influences, i.e., distance-based and transition-based influences from designed POI semantic graphs. Additionally, we propose a novel Graph-enhanced Spatial-Temporal network (GSTN) which incorporates user spatial and temporal dependencies for next POI recommendation. Specifically, GSTN consists of a Long Short-Term Memory (LSTM) network for user-specific temporal dependencies modeling and GSD for user spatial dependencies learning. Finally, we evaluate the proposed model using three real-world datasets. Extensive experiments demonstrate the effectiveness of GSD in capturing various geographical influences and the improvement of GSTN over state-of-the-art methods.

Tourism is an important industry and a popular leisure activity involving billions of tourists per annum. One challenging problem tourists face is identifying attractive Places-of-Interest (POIs) and planning the personalized trip with time constraints. Most of the existing trip recommendation methods mainly consider POI popularity and user preferences, and focus on the last visited POI when choosing the next POI. However, the visit patterns and their asymmetry property have not been fully exploited. To this end, in this paper, we present a GRM-RTrip (short for G raph-based R epresentation M ethod for R einforce Trip Recommendation) framework. GRM-RTrip learns POI representations from incoming and outgoing views to obtain asymmetric POI-POI transition probability via POI-POI graph networks, and then fuses the trained POI representation into a user-POI graph network to estimate user preferences. Finally, after formulating the personalized trip recommendation as a Markov Decision Process (MDP), we utilize a reinforcement learning algorithm for generating a personalized trip with maximal user travel experience. Extensive experiments are performed on the public datasets and the results demonstrate the superiority of GRM-RTrip compared with the state-of-the-art trip recommendation methods.

AbstractIn recent years, with the rapid development of mobile applications, user check-in histories have been increasing. Successive point-of-interest (POI) recommendation has gained growing attention. Existing successive point-of-interest recommendation methods learn long- and short-term user preferences through historical check-in sequences to provide more personalized services. However, due to sparse data and complicated temporal patterns, the application of such technique is still limited by two challenges: 1) difficulty meeting user travel needs in time; 2) difficulty capturing users complicated behavior patterns. To address this problem, we propose a new Influence-Aware successive POI recommendation Model (InfAM), which can learn the influence of POIs in a short-term sequence fragment for next point-of-interest recommendation. To capture periodic patterns of user movements, InfAM takes a user’s check-in data within a day as an input sequence to address the current travel needs of the user. In addition, based on multihead attention mechanism and user embedding, InfAM focuses on the influence of POIs in short-term sequences and general user preferences in these sequences. Therefore, InfAM integrates three specific dependencies, which can fully learn the dynamic interaction between short-term preferences: the influence of POIs in short-term sequence fragments (POI-poi), user preferences (POI-user), and the periodicity of check-ins (POI-time). Evaluation results on real-world datasets show that InfAM achieves state-of-the-art recommendation performance.