The role of location and social strength for friendship prediction in location-based social networks

Valverde-Rebaza, Jorge; Roche, Mathieu; Poncelet, Pascal; Lopes, Alneu de Andrade

doi:10.1016/j.ipm.2018.02.004

Cited by 43 publications

(17 citation statements)

References 36 publications

(89 reference statements)

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“…Some data mining methods strive to find a balance between optimizing computational resources and improving the predictive power (e.g. through the exploitation of location data) due to its role in different applications such as recommendation systems using social networking services [48].…”

Section: Data Mining and Meteorologymentioning

confidence: 99%

Explainability with Association Rule Learning for Weather Forecast

2021

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The reliability of the weather forecast models is a complex issue since it depends on numerous parameters and the technical infrastructure which supports them. In doing so, there is a need for advanced works oriented towards a better understanding of these models and the analysis of main associated parameters. Our approach is to study the applicability of the extracted association rules to provide a clearer understanding of atmospheric exchanges. In this work, the proposed methodology is based on the discovery of the interesting interpretable relationships between measured meteorological parameters at the Atmospheric Research Center of Lannemezan (South-West of France). In the preprocessing step, the proposed method is considered to be effectively flexible to account for data uncertainties, unlike the majority of classical evaluation methods mainly directed towards the reduction of variables and data redundancy. In postprocessing, the advantage of our approach is that the extracted rules are a metamodeling of interpretable useful knowledge for the clarity and conciseness of its representation. Moreover, in the processing, the interpretability in data sciences is recent and still in its infancy. The generated association rules with their statistical and semantic interpretations have globally highlighted the possibilities of explicit analysis of meteorological parameters. This study showed that among the generated relevant rules, three parameters (temperature, humidity, wind speed) have a high frequency in the antecedents of the rules and that the only consequence is rain. This is useful for the identification of potential improvements and gaps in the existing models of atmospheric observations, in particular, to understand the related parameterizations to the productivity of the rain phenomenon.

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Section: Data Mining and Meteorologymentioning

confidence: 99%

Explainability with Association Rule Learning for Weather Forecast

2021

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“…Having the check-in data, LBSNs have also been able to mine users' previous activities, extract their preferences, and dynamically recommend new POIs to the users. Due to its significance for personalization and business/service advertisement, several researchers have recently studied and enhanced different aspects of POI recommendation systems (Li et al 2014, Sun et al 2015, Litou et al 2017, Orso et al 2017, Xia et al 2017, Yu et al 2017a, Valverde-Rebaza et al 2018, Si et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

Taheri

Farnaghi

Alimohammadi

et al. 2021

Journal of Spatial Science

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Previous studies neglected the impact of shared spatio-temporal interests in the Point of Interest (POI) recommendation. The proposed GHTG-ERWR method simultaneously models shared spatio-temporal preferences of users, the dynamics of users' behavior, and the geographical effect for POI recommendation. It is composed of an Extended Random Walk with Restart (ERWR) algorithm that works on a Geographical-temporal Hybrid Tripartite Graph (GHTG). The method overcomes shortcomings of prior approaches by employing users' joint preferences over time and space through the auxiliary information derived from location-session and sessionlocation edges. Experimental results on Gowalla and Weeplaces datasets proved the feasibility of the approach.

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“…As part of online social interaction in LBSN's [19,20], users [21] can announce their geo-location [22], announce the activity performed [23], and discuss places they visit (referred as "check-in" [24]). By the third quarter of 2017, Weibo [25] amounted up to 376 million monthly active users (MAU), 172 million daily active users (DAU).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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The role of location and social strength for friendship prediction in location-based social networks

Cited by 43 publications

References 36 publications

Explainability with Association Rule Learning for Weather Forecast

Explainability with Association Rule Learning for Weather Forecast

Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

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