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How to Use the Social Media Data in Assisting Restaurant Recommendation

  • Wenjuan Cui
  • Pengfei Wang
  • Xin Chen
  • Yi Du
  • Danhuai Guo
  • Yuanchun ZhouEmail author
  • Jianhui Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)

Abstract

Online social network applications such as Twitter, Weibo, have played an important role in people’s life. There exists tremendous information in the tweets. However, how to mine the tweets and get valuable information is a difficult problem. In this paper, we design the whole process for extracting data from Weibo and develop an algorithm for the foodborne disease events detection. The detected foodborne disease information are then utilized to assist the restaurant recommendation. The experiment results show the effectiveness and efficiency of our method.

Keywords

Recommender system Event detection Social media Foodborne disease 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61402435,41371386,91224006, and the Knowledge Innovation Program of Chinese Academy of Sciences under Grant No. CNIC_QN_1507.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Sharma, L., Gera, A.: A survey of recommendation system: research challenges. Int. J. Eng. Trends Technol. (IJETT) 4(5), 1989–1992 (2013)Google Scholar
  3. 3.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)CrossRefGoogle Scholar
  4. 4.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)CrossRefGoogle Scholar
  5. 5.
    Shi, Y., Larson, M., Hanjalic, A.: Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Comput. Surv. (CSUR) 47(1), 3 (2014)CrossRefGoogle Scholar
  6. 6.
    Xie, H., Li, Q., Mao, X.: Context-aware personalized search based on user and resource profiles in folksonomies. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 97–108. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. In: ICWSM 2010, pp. 178–185 (2010)Google Scholar
  8. 8.
    Li, X., Xie, H., Song, Y., Li, Q., Zhu, S., Wang, F.: Does summarization help stock prediction? news impact analysis via summarization. IEEE Intell. Syst. 30(3), 26–34 (2015)CrossRefGoogle Scholar
  9. 9.
    Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: detecting influenza epidemics using twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1568–1576. Association for Computational Linguistics (2011)Google Scholar
  10. 10.
    Signorini, A., Segre, A.M., Polgreen, P.M.: The use of twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PloS ONE 6(5), e19467 (2011)CrossRefGoogle Scholar
  11. 11.
    Culotta, A.: Detecting influenza outbreaks by analyzing twitter messages (2010). arXiv preprint arXiv:1007.4748
  12. 12.
    Gomide, J., Veloso, A., Meira Jr., W., Almeida, V., Benevenuto, F., Ferraz, F., Teix-eira, M.: Dengue surveillance based on a computational model of spatio-temporallocality of twitter. In: Proceedings of the 3rd International Web Science Conference, p. 3. ACM (2011)Google Scholar
  13. 13.
    Center for Disease Control Prevention (CDC): CDC estimates of foodborne illness in the United States. Retrieved 23 March 2011Google Scholar
  14. 14.
    Newkirk, R.W., Bender, J.B., Hedberg, C.W.: The potential capability of social media as a component of food safety and food terrorism surveillance systems. Foodborne Pathog. Dis. 9(2), 120–124 (2012)CrossRefGoogle Scholar
  15. 15.
    Harris, J.K., Mansour, R., Choucair, B., Olson, J., Nissen, C., Bhatt, J., Brown, S.: Health department use of social media to identify foodborne illness-chicago, illinois, 2013–2014. MMWR Morb. Mortal Wkly. Rep. 63(32), 681–685 (2014)Google Scholar
  16. 16.
    Xie, H., Yu, L., Li, Q.: A hybrid semantic item model for recipe search by example. In: 2010 IEEE International Symposium on Multimedia (ISM), pp. 254–259. IEEE (2010)Google Scholar
  17. 17.
    Sadilek, A., Brennan, S., Kautz, H., Silenzio, V.: nEmesis: Which restaurants should you avoid today? In: First AAAI Conference on Human Computation and Crowd- Sourcing (2013)Google Scholar
  18. 18.
    Sadilek, A., Kautz, H., DiPrete, L., Labus, B., Portman, E., Teitel, J., Silenzio, V.: Deploying nemesis: Preventing foodborne illness by data mining social media (2016)Google Scholar
  19. 19.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781
  20. 20.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  21. 21.
    Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. Association for Computational Linguistics (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Wenjuan Cui
    • 1
  • Pengfei Wang
    • 1
    • 2
  • Xin Chen
    • 1
  • Yi Du
    • 1
  • Danhuai Guo
    • 1
  • Yuanchun Zhou
    • 1
    Email author
  • Jianhui Li
    • 1
  1. 1.Computer Network Information CenterChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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