Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Applying uncertainty theory into the restaurant recommender system based on sentiment analysis of online Chinese reviews

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

In this study, we utilize users’ reviews to a restaurant recommender system to further explore users’ opinions by the proposed recommender approach. Considering the uncertainty of users opinions, we apply the uncertain set to acquire users’ sentiment polarity, and the uncertain variable to determine users’ sentiment strength through sentiment analysis. To more accurately identify users’ opinions, a distance-based approach is designed to detect the similar reviewers’ opinions by combining sentiment polarity and sentiment strength. And then, a restaurant recommender model is proposed to evaluate the effectiveness of the presented recommendation algorithm. In experiments, we tested the performance of the recommendation algorithm with two real-world data sets. Even more remarkable, we compared the proposed user’s profile that are used in two experiments. The experiments demonstrate that the significant performance of our method in terms of increasing the accuracy of the recommender system results. These results show that user-provided reviews include richer information than ratings in the process of recommender. We also uncover the effectiveness of the uncertainty theory to characterize users’ opinions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

Notes

  1. http://git.oschina.net/wltea/IK-Analyzer-2012FF

  2. http://t.dianping.com/tianjin

References

  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)

    Article  Google Scholar 

  2. Chang, P.-T., Huang, L.-C., Lin, H.-J.: The fuzzy Delphi method via fuzzy statistics and membership function fitting and an application to the human resources. Fuzzy Sets Syst. 112(3), 511–520 (2000)

    Article  Google Scholar 

  3. Chen, X.: Variation analysis of uncertain stationary independent increment processes. Eur. J. Oper. Res. 222(2), 312–316 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chen, L., Wang, F.: Preference-based clustering reviews for augmenting e-commerce recommendation. Knowl.-Based Syst. 50, 44–59 (2013)

    Article  Google Scholar 

  5. Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-Adap. Inter. 25(2), 99–154 (2015)

    Article  Google Scholar 

  6. Ding, S.: Uncertain random newsboy problem. J. Intell. Fuzzy Syst. 26(1), 483–490 (2014)

    MathSciNet  MATH  Google Scholar 

  7. Dong, Z., Dong, Q.: How Net and the Computation of Meaning. World Scientific, Singapore (2006)

    Book  Google Scholar 

  8. Esparza, S.G., O’Mahony, M.P., Smyth, B.: Mining the real-time web: a novel approach to product recommendation. Knowl.-Based Syst. 29, 3–11 (2012)

    Article  Google Scholar 

  9. Fu, G., Wang, X.: Chinese sentence-level sentiment classification based on fuzzy sets. In: Proceedings of the 23rd international conference on computational linguistics: posters 2010, pp. 312–319. Association for Computational Linguistics (2010)

  10. Gao, Y.: Uncertain models for single facility location problems on networks. Appl. Math. Model. 36(6), 2592–2599 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Gao, Y., Qin, Z.: On computing the edge-connectivity of an uncertain graph. IEEE Trans. Fuzzy Syst. 24(4), 981–991 (2016)

    Article  Google Scholar 

  12. Gao, Y., Yu, W., Chao, P., Zhang, R., Zhou, A., Yang, X.: A restaurant recommendation system by analyzing ratings and aspects in reviews. In: Database systems for advanced applications, pp. 526–530. Springer (2015)

  13. Gao, Y., Yang, L., Li, S., Kar, S.: On distribution function of the diameter in uncertain graph. Inf. Sci. 296, 61–74 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  14. Guo, J., Sun, L., Li, W., Yu, T.: Applying uncertainty theory to group recommender systems taking account of experts preferences. Multimed. Tools Appl. (2017). https://doi.org/10.1007/s11042-11017-14922-11044

  15. Jiang, T., Yuan, B., Jiang, J., Yu, H.: Short text sentiment entropy optimization based on the fuzzy sets. In: 2015 12th Web Information System and Application Conference (WISA), pp. 247–250. IEEE (2015)

  16. Koohi, H., Kiani, K.: User based collaborative filtering using fuzzy C-means. Measurement. 91, 134–139 (2016)

    Article  Google Scholar 

  17. Liu, B.: Uncertainty theory. In: Uncertainty theory. pp. 205–234. Springer, Berlin, Heidelberg (2007)

  18. Liu, B.: Uncertainty Theory: a Branch of Mathematics for Modeling Human Uncertain. Springer-Verlag, Berlin (2010)

    Book  Google Scholar 

  19. Liu, B.: Uncertain set theory and uncertain inference rule with application to uncertain control. J. Uncertain. Syst. 4(2), 83–98 (2010)

    Google Scholar 

  20. Liu, B.: Membership functions and operational law of uncertain sets. Fuzzy Optim. Decis. Making. 11(4), 387–410 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Liu, H., He, J., Wang, T., Song, W., Du, X.: Combining user preferences and user opinions for accurate recommendation. Electron. Commer. Res. Appl. 12(1), 14–23 (2013)

    Article  Google Scholar 

  22. Sarwar, B., Karypis, G., Konstan, J., Ried, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM conference on electronic commerce 2000. pp. 158–167. ACM (2000)

  23. Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: Proceedings of the fifth international conference on computer and information technology 1, pp. 291–324 (2002)

  24. Shakhnarovich, G., Indyk, P., Darrell, T.: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. The MIT Press, Cambridge (2006)

    Book  Google Scholar 

  25. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv Artif Intell. 2009, 4 (2009)

    Article  Google Scholar 

  26. Tan, S., Zhang, J.: An empirical study of sentiment analysis for chinese documents. Expert Syst. Appl. 34(4), 2622–2629 (2008)

    Article  Google Scholar 

  27. Wang, X., Gao, Z., Guo, H.: Delphi method for estimating uncertainty distributions. Inf. Int. Interdiscip. J. 15(2), 449–460 (2012)

    MathSciNet  MATH  Google Scholar 

  28. Wu, Z., Shi, J., Lu, C., Chen, E., Xu, G., Li, G., Xie, S., Philip, S.Y.: Constructing plausible innocuous pseudo queries to protect user query intention. Inf. Sci. 325, 215–226 (2015)

    Article  Google Scholar 

  29. Wu, Z., Li, G., Liu, Q., Xu, G., Chen, E.: Covering the sensitive subjects to protect personal privacy in personalized recommendation. IEEE Trans. Serv. Comput. (99), pp. 1–1 (2016)

  30. Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML 99, pp. 412–420. (1997)

  31. Yao, K., Li, X.: Uncertain alternating renewal process and its application. IEEE Trans. Fuzzy Syst. 20(6), 1154–1160 (2012)

    Article  Google Scholar 

  32. Zadeh, L.A.: Information and control. Fuzzy Sets. 8(3), 338–353 (1965)

    Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge that this work is financed by the National Natural Science Foundation of China (grant number 71671121).

Author information

Authors and Affiliations

  1. College of Management and Economics, Tianjin University, Tianjin, 300072, People’s Republic of China

    Lihua Sun, Junpeng Guo & Yanlin Zhu

Authors
  1. Lihua Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junpeng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanlin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junpeng Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, L., Guo, J. & Zhu, Y. Applying uncertainty theory into the restaurant recommender system based on sentiment analysis of online Chinese reviews. World Wide Web 22, 83–100 (2019). https://doi.org/10.1007/s11280-018-0533-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-018-0533-x

Keywords

Search

Navigation