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Research on Information Recommendation Optimization Mechanism Based on Emotional Expression and Cognition

  • Ke Zhong
  • Liqun ZhangEmail author
  • Xiaolei Guan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10920)

Abstract

The information revolution brought by the rapid spread of the Internet has made people be more dependent on the Internet to obtain information. The internet products based on information are increasing the construction of the information recommendation system. But at present, the information recommendation systems are all limited to the content of information itself, ignoring the differences in the emotional expression of information. This research tried to purpose an approach for the information emotional semantic classification and recommendation based on emotional cognitive model. This research used news Internet product as an example to construct a quantitative coordinates of emotional cognition system through variable control, samples association analysis and statistical data analysis to improve the present information recommendation system. The results of verification experiment indicated that it is practical and effective for information classify and recommend. It is foreseeable that the theory of this research can be applied to other internet products about business, social communication and so on. Meanwhile, the research methods and results can be applied to psychology, sociology research and other specific areas, playing a guiding and testing role.

Keywords

Quantitative coordinates of emotional cognition system Recommendation system Emotional cognitive Emotional expression 

References

  1. 1.
    Fox, E.: Emotion Science Cognitive and Neuroscientific Approaches to Understanding Human Emotions. Palgrave Macmillan, Basingstoke (2008)Google Scholar
  2. 2.
    Carstensen, L.L., Pasupathi, M., Mayr, U., Nesselroade, J.R.: Emotional experience in everyday life across the adult life span. J. Pers. Soc. Psychol. 79(4), 644 (2000)CrossRefGoogle Scholar
  3. 3.
    Scherer, K.R.: What are emotions? And how can they be measured? Soc. Sci. Inf. 44(4), 695–729 (2005)CrossRefGoogle Scholar
  4. 4.
    Norman, D.A.: Emotional Design: Why We Love (or Hate) Everyday Things (2004)Google Scholar
  5. 5.
    张淼: 社会化媒体在市场营销中的应用研究. (Doctoral dissertation, 首都经济贸易大学) (2014)Google Scholar
  6. 6.
    包传颉: 移动视频,领跑3 g新应用. 信息通信技术 3(2) (2009)Google Scholar
  7. 7.
    Liang, T., Zhang, L., Xie, M.: Research on image emotional semantic retrieval mechanism based on cognitive quantification model. In: Marcus, A., Wang, W. (eds.) DUXU 2017. LNCS, vol. 10290, pp. 115–128. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58640-3_10CrossRefGoogle Scholar
  8. 8.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. ACM (2004)Google Scholar
  9. 9.
    Weaver, A.: Facebook and other pandora’s boxes. Access 24(4), 24 (2010)Google Scholar
  10. 10.
    任明: 智能信息系统:以关联知识优化数据建模的方法和实践. 浙江大学出版社 (2012)Google Scholar
  11. 11.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  12. 12.
    Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.: MovieLens unplugged: experiences with an occasionally connected recommender system. In: International Conference on Intelligent User Interfaces, pp. 263–266. ACM (2003)Google Scholar
  13. 13.
    Billsus, D.: Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5), 34–38 (2002)CrossRefGoogle Scholar
  14. 14.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: SIGCHI Conference on Human Factors in Computing Systems, vol. 1, pp. 194–201. ACM Press/Addison-Wesley Publishing Co. (1995)Google Scholar
  15. 15.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)Google Scholar
  16. 16.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: SIGCHI Conference on Human Factors in Computing Systems, vol. 110, pp. 210–217. ACM Press/Addison-Wesley Publishing Co. (1995)Google Scholar
  17. 17.
    Plume, M.L.: SPSS (statistical package for the social sciences). Encyclopedia Inf. Syst. 38(4), 187–196 (2003)CrossRefGoogle Scholar
  18. 18.
    邓以克: 基于改进的SVM-KNN算法的中文网页层次式分类. (Doctoral dissertation, 浙江大学计算机科学与技术学院 浙江大学) (2010)Google Scholar
  19. 19.
    Xie, M., Zhang, L., Liang, T.: A quantitative study of emotional experience of Daqi based on cognitive integration. In: Marcus, A., Wang, W. (eds.) DUXU 2017. LNCS, vol. 10288, pp. 306–323. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58634-2_24CrossRefGoogle Scholar
  20. 20.
    Nie, N.H., Bent, D.H., Hull, C.H.: SPSS: Statistical Package for the Social Sciences. McGraw-Hill, New York (1970)Google Scholar
  21. 21.
    Green, S.B., Salkind, N.J., Jones, T.M.: Using SPSS for Windows; Analyzing and Understanding Data. Prentice Hall PTR, Upper Saddle River (1996)Google Scholar
  22. 22.
    Fang, A., Ya-Zi, L.I.: Computing algorithm of user similarity in virtual medical communities. J. Med. Inform. (2011)Google Scholar
  23. 23.
    Wuensch, K.L.: What is a Likert Scale? And How Do You Pronounce “Likert?”. East Carolina University, 4 October 2005. Accessed 30 Apr 2009Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Design ManagementShanghai Jiao Tong UniversityShanghaiChina

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