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Effective Healthcare Advertising Using Latent Dirichlet Allocation and Inference Engine

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Advances in Information Retrieval (ECIR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9022))

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Abstract

The growing access to healthcare websites has aroused the interest of designing a specific advertising system focusing on healthcare products. In this paper, we develop an advertising method which analyzes the messages posted by users on a healthcare website. The method integrates semantic analysis with an inference engine for effective healthcare advertising. Based on our experiment results, healthcare advertising systems could be enhanced by using the domain-specific knowledge to augment the content of user messages and ads.

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References

  1. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  2. Broder, A.Z., Ciccolo, P., Fontoura, M., Gabrilovich, E., Josifovski, V., Riedel, L.: Search advertising using web relevance feedback. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 1013–1022. ACM, New York (2008)

    Google Scholar 

  3. Chatterjee, P., Hoffman, D.L., Novak, T.P.: Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science 22(4), 520–541 (2003)

    Article  Google Scholar 

  4. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National academy of Sciences of the United States of America 101(suppl. 1), 5228–5235 (2004)

    Article  Google Scholar 

  5. Luo, G., Thomas, S.B., Tang, C.: Automatic home medical product recommendation. Journal of Medical Systems 36(2), 383–398 (2012)

    Article  Google Scholar 

  6. Phan, X.H., Nguyen, C.T., Le, D.T., Nguyen, L.M., Horiguchi, S., Ha, Q.T.: A hidden topic-based framework toward building applications with short Web documents. IEEE Transactions on Knowledge and Data Engineering 23(7), 961–976 (2011)

    Article  Google Scholar 

  7. Phuong, D.V., Phuong, T.M.: A keyword-topic model for contextual advertising. In: Proceedings of the Third Symposium on Information and Communication Technology, pp. 63–70. ACM, New York (2012)

    Google Scholar 

  8. Raghavan, H., Iyer, R.: Probabilistic first pass retrieval for search advertising: from theory to practice. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1019–1028. ACM, New York (2010)

    Google Scholar 

  9. Russell, S., Norvig, P.: Artificial Intelligence: A Modefrn Approach, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2010)

    Google Scholar 

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Li, YC., Chen, C.C. (2015). Effective Healthcare Advertising Using Latent Dirichlet Allocation and Inference Engine. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_74

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  • DOI: https://doi.org/10.1007/978-3-319-16354-3_74

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16353-6

  • Online ISBN: 978-3-319-16354-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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