Abstract
A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. In this paper, we present MobiFeed that is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, location prediction, relevance measure, and news feed scheduler. The location prediction function is designed to estimate a mobile user’s locations based on a path prediction algorithm. The relevance measure function is implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. The news feed scheduler works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. We propose a heuristic algorithm as well as an optimal algorithm for the location-aware news feed scheduler. The performance of MobiFeed is evaluated through extensive experiments using a real road map and a real social network data set. The scalability of MobiFeed is also investigated using a synthetic data set. Experimental results show that MobiFeed obtains a relevance score two times higher than the state-of-the-art approach, and it can scale up to a large number of geo-tagged messages.
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Here the cell domain length is set as the largest distance between the cell’s center and its boundary.
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Acknowledgments
Wenjian Xu and Chi-Yin Chow were supported by Guangdong Natural Science Foundation of China under Grant S2013010012363 and a research grant (CityU Project No. 9680117). Man Lung Yiu was supported by grant GRF 5302/12E from Hong Kong RGC.
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Xu, W., Chow, CY., Yiu, M.L. et al. MobiFeed: A location-aware news feed framework for moving users. Geoinformatica 19, 633–669 (2015). https://doi.org/10.1007/s10707-014-0223-5
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DOI: https://doi.org/10.1007/s10707-014-0223-5