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.
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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)
Sharma, L., Gera, A.: A survey of recommendation system: research challenges. Int. J. Eng. Trends Technol. (IJETT) 4(5), 1989–1992 (2013)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)
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)
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)
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)
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)
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)
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)
Culotta, A.: Detecting influenza outbreaks by analyzing twitter messages (2010). arXiv preprint arXiv:1007.4748
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)
Center for Disease Control Prevention (CDC): CDC estimates of foodborne illness in the United States. Retrieved 23 March 2011
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)
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)
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)
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)
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)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781
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)
Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. Association for Computational Linguistics (2004)
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.
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Cui, W. et al. (2016). How to Use the Social Media Data in Assisting Restaurant Recommendation. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_12
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DOI: https://doi.org/10.1007/978-3-319-32055-7_12
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