Mining of Location-Based Social Networks for Spatio-Temporal Social Influence

Wen, Yu Ting; Fan, Yi; Peng, Wen-Chih

doi:10.1007/978-3-319-57454-7_62

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Closely related, Check-in Prediction [16] tries to predict future check-ins, which is useful in marketing applications. Another research field is LBSN-based Friend Recommendation or Social Link Discovery [21], which suggest new friends to users that have a similar interest at similar locations, while also having similar social connections. Other research topics include finding communities [25] and efficient query processing [10] in LBSNs.…”

Section: Location-based Social Network: An Overviewmentioning

confidence: 99%

Location-Based Social Simulation

Kavak

Kim

Crooks

et al. 2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

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Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN datasets in such studies has severe weaknesses: sparse and small datasets, privacy concerns, and a lack of authoritative ground-truth. Our vision is to create a large scale geo-simulation framework to simulate human behavior and to create synthetic but realistic LBSN data that captures the location of users over time as well as social interactions of users in a social network. While existing LBSN datasets are trivially small, such a framework would provide the first source of massive LBSN benchmark data which would closely mimic the real world, containing high-fidelity information of location, and social connections of millions of simulated agents over several years of simulated time. Therefore, it would serve the research community by revitalizing and reshaping research on LBSNs by allowing researchers to see the (simulated) world through the lens of an omniscient entity having perfect data. These evaluations will guide future research enabling us to develop solutions to improve LBSN applications such as user-location recommendation, friend recommendation, location prediction, and location privacy. CCS CONCEPTS • Human-centered computing → Social networks; • Computing methodologies → Agent / discrete models; • Information systems → Location based services.

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Section: Location-based Social Network: An Overviewmentioning

confidence: 99%

Location-Based Social Simulation

Kavak

Kim

Crooks

et al. 2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

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“…The steering clear of entire extraneous is used to short list the subset of most leading features and increase the precision and also speed up the model training duration, are the application of classifier [14]. Data mining is the most significant method for inspiring data from the vast amount of data packet in data warehouses, parallel data storehouses and other databases [15][16]. Knowledge discovery, preprocessing, data selection, transformation in data mining and cleaning are the steps which are used and also it is said to be an iterative process [16].…”

Section: Introductionmentioning

confidence: 99%

Random Forest with Harmony Search Optimization for Location Based Advertising

Rohilla¹,

Chakraborty²,

Kumar³

2019

IJITEE

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Location based services are essential for the mobile user for obtaining the information based on the specific location. Initially, the location information is obtained using the GIS system for getting accurate information about the location. Then relevant information is extracted based on the location then the features are extracted from the dataset. Extracted features are segmented in order to simplify the process of location based advertising. The segmented features are used for simplifying the processing of retrieved data. The random forest algorithm is utilized to effectively retrieve the user data. It is one of the Meta heuristic algorithms which provide best result for the retrieval process. The optimal solution is needed for the designed algorithm for the purpose of avoiding local optimization, thus it improves the performance with respect to accuracy. The optimization algorithm Harmony search is used for getting optimal result based on the location. The exact information for the query is obtained for the proposed approach based on the location.

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“…The social influence propels users to take in behaviours shown by their neighbours. In other words, the stronger the relationship between two users is, the more effective the prediction is in affecting the user [6].…”

Section: Introductionmentioning

confidence: 99%

Social Influence Aware Personalized Recommendation

2018

Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering

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In this work, to exploit social influence in social influence aware music recommendation, we first quantify social influence. As a step further, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

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Mining of Location-Based Social Networks for Spatio-Temporal Social Influence

Cited by 8 publications

References 12 publications

Location-Based Social Simulation

Location-Based Social Simulation

Random Forest with Harmony Search Optimization for Location Based Advertising

Social Influence Aware Personalized Recommendation

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