Abstract
The Internet provides users with an overwhelming amount of information. For this reason they may not always be able to find the information they are looking for. Recommendation systems help users locate useful information and save time. Twitter is one of the social networks that implements this type of system in order to help its users in searching content. However, the traditional recommendation system implemented by Twitter only considers people from the user’s surroundings or it suggests the followees/followers of the user’s followees. Many use Twitter as a source of information, it is therefore necessary to create a recommendation system that would suggest experts profiles to other users. Experts must be capable of providing interesting information to users. The “expert” recommended to a users will be chosen on the basis of the content they publish and whether this content is of interest to the user. The proposed system offers accurate and suitable recommendations.
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Acknowledgement
This work has been supported by project MOVIURBAN: Máquina social para la gestión sostenible de ciudades inteligentes: movilidad urbana, datos abiertos, sensores móviles. SA070U 16. Project co-financed with Junta Castilla y León, Consejería de Educación and FEDER funds.
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Jiménez-Bravo, D.M., De Paz, J.F., Villarrubia, G. (2019). Twitter’s Experts Recommendation System Based on User Content. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_28
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DOI: https://doi.org/10.1007/978-3-319-99608-0_28
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