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Ontology-Based Advertisement Recommendation in Social Networks

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Book cover Distributed Computing and Artificial Intelligence, 15th International Conference (DCAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 800))

Abstract

With the advent of the Web 2.0 era, a new source of a vast amount of data about users become available. Advertisement recommendation systems are among the applications that can benefit from these data since they can help gain a better understanding of the users’ interests and preferences. However, new challenges emerge from the need to deal with heterogeneous data from disparate sources. Semantic technologies, in general, and ontologies, in particular, have proved effective for knowledge management and data integration. In this work, an ontology-based advertisement recommendation system that leverages the data produced by users in social networking sites is proposed.

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Notes

  1. 1.

    https://www.w3.org/TR/owl2-overview/.

  2. 2.

    https://curlie.org/.

  3. 3.

    General Architecture for Text Engineering, https://gate.ac.uk/.

References

  1. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work - CSCW 1994, pp. 175–186. ACM Press, New York (1994)

    Google Scholar 

  2. Carrer-Neto, W., Hernández-Alcaraz, M.L., Valencia-García, R., García-Sánchez, F.: Social knowledge-based recommender system. Application to the movies domain. Expert Syst. Appl. 39, 10990–11000 (2012)

    Article  Google Scholar 

  3. Eirinaki, M., Gao, J., Varlamis, I., Tserpes, K.: Recommender systems for large-scale social networks: a review of challenges and solutions. Futur. Gener. Comput. Syst. 78, 413–418 (2018)

    Article  Google Scholar 

  4. Jin, S.V.: “Celebrity 2.0 and beyond!” Effects of Facebook profile sources on social networking advertising. Comput. Hum. Behav. 79, 154–168 (2018)

    Article  Google Scholar 

  5. Shadbolt, N., Berners-Lee, T., Hall, W.: The semantic web revisited. IEEE Intell. Syst. 21, 96–101 (2006)

    Article  Google Scholar 

  6. Lagos-Ortiz, K., Medina-Moreira, J., Paredes-Valverde, M.A., Espinoza-Morán, W., Valencia-García, R.: An ontology-based decision support system for the diagnosis of plant diseases. J. Inf. Technol. Res. 10, 42–55 (2017)

    Article  Google Scholar 

  7. del Pilar Salas-Zárate, M., Valencia-García, R., Ruiz-Martínez, A., Colomo-Palacios, R.: Feature-based opinion mining in financial news: an ontology-driven approach. J. Inf. Sci. 43, 458–479 (2017)

    Article  Google Scholar 

  8. Boratto, L., Carta, S., Fenu, G., Saia, R.: Semantics-aware content-based recommender systems: design and architecture guidelines. Neurocomputing 254, 79–85 (2017)

    Article  Google Scholar 

  9. Kunaver, M., Požrl, T.: Diversity in recommender systems – a survey. Knowl.-Based Syst. 123, 154–162 (2017)

    Article  Google Scholar 

  10. Aggarwal, C.C.: An introduction to recommender systems. In: Recommender Systems, pp. 1–28. Springer International Publishing, Cham (2016)

    Chapter  Google Scholar 

  11. Lalwani, D., Somayajulu, D.V.L.N., Krishna, P.R.: A community driven social recommendation system. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 821–826. IEEE (2015)

    Google Scholar 

  12. Aguilar, J., Valdiviezo-Díaz, P., Riofrio, G.: A general framework for intelligent recommender systems. Appl. Comput. Inform. 13, 147–160 (2017)

    Article  Google Scholar 

  13. Nakatsuji, M., Yoshida, M., Ishida, T.: Detecting innovative topics based on user-interest ontology. Web Semant. Sci. Serv. Agents World Wide Web 7, 107–120 (2009)

    Article  Google Scholar 

  14. Mezghani, M., Péninou, A., Zayani, C.A., Amous, I., Sèdes, F.: Producing relevant interests from social networks by mining users’ tagging behaviour: a first step towards adapting social information. Data Knowl. Eng. 108, 15–29 (2017)

    Article  Google Scholar 

  15. Kang, J., Lee, H.: Modeling user interest in social media using news media and Wikipedia. Inf. Syst. 65, 52–64 (2017)

    Article  Google Scholar 

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Acknowledgements

This work has been supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through project KBS4FIA (TIN2016-76323-R).

Author information

Authors and Affiliations

  1. Dpto. Informática y Sistemas, Facultad de Informática, Universidad de Murcia, Murcia, Spain

    Francisco García-Sánchez, José Antonio García-Díaz & Rafael Valencia-García

  2. Departamento de Informática, Universidad Carlos III de Madrid, Leganés, Spain

    Juan Miguel Gómez-Berbís

Authors
  1. Francisco García-Sánchez
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  2. José Antonio García-Díaz
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  3. Juan Miguel Gómez-Berbís
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  4. Rafael Valencia-García
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Corresponding author

Correspondence to Francisco García-Sánchez .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub. Edificio Multimedia I+D+i, University of Salamanca, Salamanca, Spain

    Fernando De La Prieta

  2. Faculty of Engineering, Department of Electronics, Information and Communication, Osaka Institute of Technology, Osaka, Osaka, Japan

    Sigeru Omatu

  3. Department of Computing Systems, School of Industrial Engineers at Albacete, University of Castilla-La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

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García-Sánchez, F., García-Díaz, J.A., Gómez-Berbís, J.M., Valencia-García, R. (2019). Ontology-Based Advertisement Recommendation in Social Networks. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_5

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