point of interest
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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.

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.

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.

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.

AbstractAs the use of spectral/hp element methods, and high-order finite element methods in general, continues to spread, community efforts to create efficient, optimized algorithms associated with fundamental high-order operations have grown. Core tasks such as solution expansion evaluation at quadrature points, stiffness and mass matrix generation, and matrix assembly have received tremendous attention. With the expansion of the types of problems to which high-order methods are applied, and correspondingly the growth in types of numerical tasks accomplished through high-order methods, the number and types of these core operations broaden. This work focuses on solution expansion evaluation at arbitrary points within an element. This operation is core to many postprocessing applications such as evaluation of streamlines and pathlines, as well as to field projection techniques such as mortaring. We expand barycentric interpolation techniques developed on an interval to 2D (triangles and quadrilaterals) and 3D (tetrahedra, prisms, pyramids, and hexahedra) spectral/hp element methods. We provide efficient algorithms for their implementations, and demonstrate their effectiveness using the spectral/hp element library Nektar++ by running a series of baseline evaluations against the ‘standard’ Lagrangian method, where an interpolation matrix is generated and matrix-multiplication applied to evaluate a point at a given location. We present results from a rigorous series of benchmarking tests for a variety of element shapes, polynomial orders and dimensions. We show that when the point of interest is to be repeatedly evaluated, the barycentric method performs at worst $$50\%$$ 50 % slower, when compared to a cached matrix evaluation. However, when the point of interest changes repeatedly so that the interpolation matrix must be regenerated in the ‘standard’ approach, the barycentric method yields far greater performance, with a minimum speedup factor of $$7\times $$ 7 × . Furthermore, when derivatives of the solution evaluation are also required, the barycentric method in general slightly outperforms the cached interpolation matrix method across all elements and orders, with an up to $$30\%$$ 30 % speedup. Finally we investigate a real-world example of scalar transport using a non-conformal discontinuous Galerkin simulation, in which we observe around $$6\times $$ 6 × speedup in computational time for the barycentric method compared to the matrix-based approach. We also explore the complexity of both interpolation methods and show that the barycentric interpolation method requires $${\mathcal {O}}(k)$$ O ( k ) storage compared to a best case space complexity of $${\mathcal {O}}(k^2)$$ O ( k 2 ) for the Lagrangian interpolation matrix method.

Residing in the data age, researchers inferred that huge amount of geo-tagged data is available and identified the importance of Spatial Skyline queries. Spatial or geographic location in conjunction with textual relevance plays a key role in searching Point of Interest (POI) of the user. Efficient indexing techniques like R-Tree, Quad Tree, Z-order curve and variants of these trees are widely available in terms of spatial context. Inverted file is the popular indexing technique for textual data. As Spatial skyline query aims at analyzing both spatial and skyline dominance, there is a necessity for a hybrid indexing technique. This article presents the review of spatial skyline queries evaluation that include a range of indexing techniques which concentrates on disk access, I/O time, CPU time. The investigation and analysis of studies related to skyline queries based upon the indexing model and research gaps are presented in this review.

The present study aimed to debate a topic less addressed by most people, which involved research on a group of 20 students from rural areas, aged 10–14 years, which consists of performing two tests, namely the Ruler drop test and the Hand-eye coordination test, which aims at the reaction speed of the dominant and non-dominant hand and also the hand-eye coordination capacity of the subjects. The paper aimed to identify whether somatic factors and age influence the results of the group. In order to perform the two tests, it was necessary, for the beginning, information related to the study group, information on weight, age, height, dominant hand, respectively dominant eye. These represented the point of interest of the research, being reported individually to the test results, thus constituting the study basis of statistics. After obtaining the results, we concluded that a significant significance is encountered when comparing the dominant hand with the non-dominant one, obtaining a positive value for the dominant hand. At the same time, we interpreted after the research that females tend to have a much faster reaction speed, more significant than the males when it comes to using the non-dominant hand. The hypothesis was confirmed, with differences in somatic factors’ influence, but the others do not show significant values except those stated above. In addition to the practical part, the research involves an interesting theoretical foundation being reached aspects related to proprioception, coordination, speed, ways of using tests, and the opinion of other researchers who have conducted similar studies.

Abstract With the rapid development of mobile communication technology, there is a growing demand for high-quality point of interest(POI) recommendation. The POIs visited by users only account for a very small proportion. Thus traditional POI recommendation method is vulnerable to data sparsity and lacks a clear and effective explanation for POI ranking result. The POI selection made by the user is influenced by various contextual attributes. The challenge lies in representing accurately and aggregating multiple contextual information efficiently. We transform the POI recommendation into a contextual multi-attribute decision problem based on the neutrosophic set (NS) which is suitable for representing fuzzy decision information. We establish a unified framework of contextual information. Firstly, we propose a contextual multi-attribute NS transformation model of POI, including the NS model for single-dimensional attributes and the NS model for multi-dimensional attributes. And then through the aggregation of multi attribute NS, the POI that best conforms to user's preferences is recommended. Finally, the experimental results based on the Yelp dataset show that the proposed strategy performs better than the typical POI recommendation method in NDCG, accuracy, and recall rate.

Goa Tenggar or Tenggar Cave is situated in the karstic physiography of southern Tulungagung, East Java that made up of prehistoric caves. These include the Wajak complex (minimum age of 37.4 to 28.5 thousand years ago) and the Song Gentong (around 7000 BP). The formation of Tenggar Cave is influenced by the subterranean river that penetrates the limestone unit. This cave has a front width of ± 10 m and a roof height of ± 8 m. The east side of the cave floor is a layer of soil, and the western side is the river. The inside of the cave composed by very compact conglomerate deposits and paleosoil that contains faunal remains, including Cervus sp., Bos sp., Bubalus sp., and Bibos sp., which may have occurred during the Pleistocene. The fossilized faunal remains from Tenggar Cave show that there was a relatively open environment during that time, such as a savannah with large trees and flowing rivers around the cave. The paleoenvironment indicates late Pleistocene to early Holocene period, similar to paleoenvironment in the Sewu Mountains that stretch along the southern part Java from central to the eastern tip of the island includes the coastal towns of Gunung Kidul, Pacitan and Tulungagung. The situation is certainly a point of interest when associating the findings with the surrounding sites, starting from Wajak, Song Gentong, Pacitan, Ponorogo, and Gunung Kidul. However, absolute dating test is necessary to be more certain of the lifetime of the fossilized fauna. If the fossils were from the Late Pleistocene, it could be an important information for the fields of paleontology, paleoanthropology, and prehistoric archaeology given that the occurrence of sites with such antiquity are limited in Southeast Asia. It is essential to conduct intensive research in Tenggar Cave in the future.

Point of Interest (center of attention) becomes important for identity on product labels for Sokaraja fried getuk packaging. On each label, Sokaraja fried getuk uses a different center of attention from one label to another. This study aims to determine the role of the center of interest in the fried getuk label. The research method used is descriptive qualitative research by extracting data from primary and secondary sources such as interviews and literature studies. Through the perspective of the theory of graphic elements and design principles as the unit of analysis for the label of getuk goreng Sukaraja. Based on data analysis, it was concluded that the Sokaraja Fried Getuk Label has the characteristics of traditional visual elements and is dominated by red. Aspects of typography tend to be serif, sans serif, and script, using symmetrical and asymmetrical compositions. Design principles such as unity, proportion, balance, rhythm, and emphasis are visual elements that are composed quite well.