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Authors: Ahmed Galal and Abeer El-Korany

Affiliation: Faculty of Computers and Information and Cairo University, Egypt

Keyword(s): Dynamic User Modeling, Location Prediction, Semantic Context, Topical Interest, User Behavior.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Collaboration and e-Services ; e-Business ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Semantic Web ; Soft Computing ; Symbolic Systems ; Web Information Systems and Technologies ; Web Intelligence

Abstract: Prediction of user interest and behavior is currently an important research area in social network analysis. Most of the current prediction frameworks rely on analyzing user’s published contents and user’s relationships. Recently the dynamic nature of user’s modelling has been introduced in the prediction frameworks. This dynamic nature would be represented by time tagged attributes such as posts or location check-ins. In this paper, we study the relationships between geo-location information published by users at different times. This geo-location information was used to model user’s interest and behavior in order to enhance prediction of user locations. Furthermore, semantic features such as topics of interest and location category were extracted from this information in order to overcome sparsity of data. Several experiments on real twitter dataset showed that the proposed context-based prediction model which applies machine learning techniques outperformed traditional probabilist ic location prediction model that only rely on words extracted from tweets associated with specific locations. (More)

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Paper citation in several formats:
Galal, A. and El-Korany, A. (2016). Enabling Semantic User Context to Enhance Twitter Location Prediction. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-172-4; ISSN 2184-433X, SciTePress, pages 223-230. DOI: 10.5220/0005749502230230

@conference{icaart16,
author={Ahmed Galal. and Abeer El{-}Korany.},
title={Enabling Semantic User Context to Enhance Twitter Location Prediction},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2016},
pages={223-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005749502230230},
isbn={978-989-758-172-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Enabling Semantic User Context to Enhance Twitter Location Prediction
SN - 978-989-758-172-4
IS - 2184-433X
AU - Galal, A.
AU - El-Korany, A.
PY - 2016
SP - 223
EP - 230
DO - 10.5220/0005749502230230
PB - SciTePress

- Science and Technology Publications, Lda.
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