Proceedings ArticleDOI
Users location prediction in location-based social networks
Jarana Manotumruksa
- pp 44-47
TLDR
This paper proposes a model that leverages the use of Global Temporal Preferences and Spatial Correlation, to help make predictions for a previously unseen user - the so-called cold-start problem.Abstract:
The wealth of user-generated data in Location-Based Social Networks (LBSNs) has opened new opportunities for researchers to model and understand human mobile behaviour, including predicting where they are most likely to check-in next. In this paper, we propose a model that leverages the use of Global Temporal Preferences and Spatial Correlation, to help make predictions for a previously unseen user - the so-called cold-start problem. The experimental results on a real-world LBSN dataset show that our proposed model outperforms the state-of-the-art approaches on prediction accuracy and can alleviate the cold-start problem.read more
Citations
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Journal ArticleDOI
An Enhancement on Mobile Social Network using Social Link Prediction with Improved Human Trajectory Internet Data Mining
B. Suryakumar,E. Ramadevi +1 more
TL;DR: The IMC-TEM-SUCM is proposed with the SLP mechanism for identifying the relationship between two nodes and predicting the stable links and the performance effectiveness of the proposed model is evaluated through the experimental results using different real-world datasets.
Proceedings ArticleDOI
Influence-Time-Proximity Driven Locations Recommendation Model: An Integrated Approach
TL;DR: This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations to generate a ranked list of locations which will be recommended to the user.
References
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