scholarly journals Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2673
Author(s):  
Chonghuan Xu ◽  
Dongsheng Liu ◽  
Xinyao Mei
Keyword(s):  
Estimation Method ◽  
Geographical Location ◽  
Heterogeneous Data ◽  
User Preferences ◽  
Data Sets ◽  
User Characteristics ◽  
Model Studies ◽  
Poi Recommendation ◽  
Temporal Factors ◽  
The Impact

The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.

scholarly journals Analysing the Impact of Large Data Imports in OpenStreetMap

2021 ◽  
Vol 10 (8) ◽  
pp. 528
Author(s):  
Raphael Witt ◽  
Lukas Loos ◽  
Alexander Zipf
Keyword(s):  
Large Data ◽  
Heterogeneous Data ◽  
Geographical Information ◽  
Data Sets ◽  
Heterogeneous Data Source ◽  
External Data ◽  
Time Period ◽  
Global Mapping ◽  
Data Source ◽  
The Impact

OpenStreetMap (OSM) is a global mapping project which generates free geographical information through a community of volunteers. OSM is used in a variety of applications and for research purposes. However, it is also possible to import external data sets to OpenStreetMap. The opinions about these data imports are divergent among researchers and contributors, and the subject is constantly discussed. The question of whether importing data, especially large quantities, is adding value to OSM or compromising the progress of the project needs to be investigated more deeply. For this study, OSM’s historical data were used to compute metrics about the developments of the contributors and OSM data during large data imports which were for the Netherlands and India. Additionally, one time period per study area during which there was no large data import was investigated to compare results. For making statements about the impacts of large data imports in OSM, the metrics were analysed using different techniques (cross-correlation and changepoint detection). It was found that the contributor activity increased during large data imports. Additionally, contributors who were already active before a large import were more likely to contribute to OSM after said import than contributors who made their first contributions during the large data import. The results show the difficulty of interpreting a heterogeneous data source, such as OSM, and the complexity of the project. Limitations and challenges which were encountered are explained, and future directions for continuing in this field of research are given.

scholarly journals Cost of corruption and efficiency in employment of firms: The case in Vietnam

Accounting ◽  
2021 ◽  
pp. 609-614
Author(s):  
Vu Cam Nhung ◽  
Lai Cao Mai Phuong
Keyword(s):  
Waiting Time ◽  
Estimation Method ◽  
Least Square ◽  
Data Sets ◽  
Return On Equity ◽  
Average Value ◽  
Fixed Assets ◽  
Administrative Procedures ◽  
Gls Estimation ◽  
The Impact

This paper examines the impact of corruption on employers' efficiency in Vietnamese firms. The Generalized Least Square (GLS) estimation method was used for data sets surveyed for Vietnamese firms in 63 localities. The research results show that the unofficial costs in the industry and the total informal costs accounting for 10% or more of revenue will negatively affect the labor efficiency of these enterprises. For costs related to administrative procedures, businesses accept to pay these fees in order to save waiting time and it contributes to increase the efficiency of employers in businesses. In addition to the corruption factor, the study also shows that the number of employees, the location of operation, the average value of fixed assets per employee and the return on equity also affect the efficiency of use. employees in Vietnamese enterprises.

scholarly journals Exploring IoT Location Information to Perform Point of Interest Recommendation Engine: Traveling to a New Geographical Region

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 992 ◽  
Author(s):  
Xu Yang ◽  
Billy Zimba ◽  
Tingting Qiao ◽  
Keyan Gao ◽  
Xiaoya Chen
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Real Data ◽  
User Preferences ◽  
Data Sets ◽  
Wireless Internet ◽  
User Reviews ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Geographical Regions

With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) domain have begun to use social media and location sensing information to implement personalized Points-of-interests (POI) recommendations. However, this approach may fall short when a user moves to a new district or city where they have little or no activity history and social network friend information. Thus, a need to reconsider how we model the factors influencing a user’s preferences in new geographical regions in order to make personalized and relevant recommendation. A POI in LBSNs is semantically enriched with annotations such as place categories, tags, tips or user reviews which implies knowledge about the nature of the place as well as a visiting person’s interests. This provides us with opportunities to better understand the patterns in users’ interests and activities by exploiting the annotations which will continue to be useful even when a user moves to unfamiliar places. In this research, we proposed a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting. Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. We use real data sets partitioned by city provided by Yelp, to compare the accuracy of our proposed method against some baseline POI recommendation algorithms. Experimental results show that our algorithm achieves a better accuracy.

scholarly journals POI recommendation method using LSTM-attention in LBSN considering privacy protection

2021 ◽  
Author(s):  
Kun Wang ◽  
Xiaofeng Wang ◽  
Xuan Lu
Keyword(s):  
Deep Learning ◽  
Privacy Protection ◽  
Geographical Location ◽  
User Preferences ◽  
Sequence Length ◽  
Short Term ◽  
Network Information ◽  
Poi Recommendation ◽  
Location Based Social Networks ◽  
Better Than

AbstractAiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

scholarly journals Machine learning, artificial neural networks and social research

2020 ◽  
Author(s):  
Giovanni Di Franco ◽  
Michele Santurro
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Analysis ◽  
Social Research ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Artificial Neural ◽  
Analysis Models ◽  
The Impact

Abstract Machine learning (ML), and particularly algorithms based on artificial neural networks (ANNs), constitute a field of research lying at the intersection of different disciplines such as mathematics, statistics, computer science and neuroscience. This approach is characterized by the use of algorithms to extract knowledge from large and heterogeneous data sets. In addition to offering a brief introduction to ANN algorithms-based ML, in this paper we will focus our attention on its possible applications in the social sciences and, in particular, on its potential in the data analysis procedures. In this regard, we will provide three examples of applications on sociological data to assess the impact of ML in the study of relationships between variables. Finally, we will compare the potential of ML with traditional data analysis models.

scholarly journals Game theoretical inference of human behavior in social networks

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Nicolò Pagan ◽  
Florian Dörfler
Keyword(s):  
Social Networks ◽  
Network Formation ◽  
Preferential Attachment ◽  
Estimation Method ◽  
Small World ◽  
User Preferences ◽  
Clustering Coefficient ◽  
Real World Data ◽  
Trade Offs ◽  
The Impact

AbstractSocial networks emerge as a result of actors’ linking decisions. We propose a game-theoretical model of socio-strategic network formation on directed weighted graphs, in which every actors’ benefit is a parametric trade-off between centrality measure, brokerage opportunities, clustering coefficient, and sociological network patterns. We use two different stability definitions to infer individual behavior of homogeneous, rational agents from network structure, and to quantify the impact of cooperation. Our theoretical analysis confirms results known for specific network motifs studied previously in isolation, yet enables us to precisely quantify the trade-offs in the space of user preferences. To deal with complex networks of heterogeneous and irrational actors, we construct a statistical behavior estimation method using Nash equilibrium conditions. We provide evidence that our results are consistent with empirical, historical, and sociological observations on real-world data-sets. Furthermore, our method offers sociological and strategic interpretations of random networks models, such as preferential attachment and small-world networks.

Development of interactive diagnostic tools and metrics for the socio-economic consequences of floods

2021 ◽  
Author(s):  
Annie-Claude Parent ◽  
Frédéric Fournier ◽  
François Anctil ◽  
Brian Morse ◽  
Jean-Philippe Baril-Boyer ◽  
...  
Keyword(s):  
Maximum Flow ◽  
Large Data ◽  
Heterogeneous Data ◽  
Economic Consequences ◽  
Disaster Mitigation ◽  
Diagnostic Tools ◽  
Data Sets ◽  
Residential Areas ◽  
Level Data ◽  
The Impact

<p>Spring floods have generated colossal damages to residential areas in the Province of Quebec, Canada, in 2017 and 2019. Government authorities need accurate modelling of the impact of theoretical floods in order to prioritize pre-disaster mitigation projects to reduce vulnerability. They also need accurate modelling of forecasted floods in order to direct emergency responses. </p><p>We present a governmental-academic collaboration that aims at modelling flood impact for both theoretical and forecasted flooding events over all populated river reaches of meridional Quebec. The project, funded by the ministère de la Sécurité publique du Québec (Quebec ministry in charge of public security), consists in developing a diagnostic tool and methods to assess the risk and impacts of flooding. Tools under development are intended to be used primarily by policy makers. </p><p>The project relies on water level data based on the hydrological regimes of nearly 25,000 km of rivers, on high-precision digital terrain models, and on a detailed database of building footprints and characterizations. It also relies on 24h and 48h forecasts of maximum flow for the subject rivers. The developed tools integrate large data sets and heterogeneous data sources and produce insightful metrics on the physical extent and costs of floods and on their impact on the population. The software also provides precise information about each building affected by rising water, including an estimated cost of the damages and impact on inhabitants.  </p>

Assessment of the influence of the spatial environment on the socio-economic development of the municipality

2019 ◽  
Vol 40 (3) ◽  
pp. 148-155
Author(s):  
K. N. Yusupov ◽  
V. M. Timiryanova, ◽  
Iu. S. Toktamysheva ◽  
A. F. Zimin,
Keyword(s):  
Economic Development ◽  
Socioeconomic Development ◽  
Statistical Data ◽  
Geographical Location ◽  
Integrated Approach ◽  
The State ◽  
Municipal District ◽  
Socio Economic Development ◽  
Spatial Environment ◽  
The Impact

The article presents a methodology for assessing the impact of spatial environment on the socioeconomic development of municipalities. It relies on existing tools for assessing the state and potential of the geographical location of municipalities. An integrated approach allows to determine the potential of the interaction of the municipality with the neighbors of the first and second order. The methodology was tested on statistical data on the Blagovarsky municipal district.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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