Random walk based co-occurrence prediction in location-based social networks

In this paper, we propose a new version of the LBRW (Learning based Random Walk), LBRW-Co, for predicting users co-occurrence based on mobility homophily and social links. More precisely, we analyze and mine jointly spatio-temporal and social features with the aim to predict and rank users co-occurr...

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Bibliographic Details
Main Authors: Mourchid, Fatima, Kobbane, Abdellatif, Ben Othman, Jalel, El Koutbi, Mohammed
Format: Conference Proceeding
Language:English
Subjects:
Co-occurrence prediction
Data mining
Internet
Link prediction
Location Based Social Networks (LBSN)
Logistics
Measurement
Next generation networking
Prediction algorithms
Predictive models
Random walk
Social network services
User patterns
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Description
Summary:In this paper, we propose a new version of the LBRW (Learning based Random Walk), LBRW-Co, for predicting users co-occurrence based on mobility homophily and social links. More precisely, we analyze and mine jointly spatio-temporal and social features with the aim to predict and rank users co-occurrences. Experiments are performed on the Foursquare LBSN with accurate and refined measurements. Experimental results demonstrate that our LBRW-Co model have substantial advantages over baseline approaches in predicting and ranking co-occurrence interactions.
ISSN:1938-1883
DOI:10.1109/ICC.2017.7997209