Advertisement

A New Recommendation Framework for Accommodations Sharing Based on User Preference

  • Qiuyan ZhongEmail author
  • Yangguang Wang
  • Yueyang Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 660)

Abstract

The population of accommodations sharing makes it necessary to make personalized recommendations for users. But general methods perform poorly when applied to accommodations sharing due to severer sparse data and user stickiness. In order to conquer these gaps, this study proposes a specific framework with considering the information of user preference through LDA and Naive Bayes Method. These two methods enable to transfer the user preference into quantitative analysis. Furthermore, LFM possesses the function that can forecast the missing parts through learning user-item rating matrices. It generates the first recommendation list based on the quantized characterization of user preference. The second recommendation list can produce by the Naive Bayes Method. Consequently, the final optimal recommendation result of accommodation sharing is gathered from combining two recommendation lists. Finally, experiments based on the Airbnb real dataset demonstrate the promising potential of this study.

Keywords

Accommodations sharing Recommendation system Latent Factor Model (LFM) Latent Dirichlet Allocation (LDA) User preference analysis 

Notes

Acknowledgements

This research is financially supported by NSFC with its projects (71533001).

References

  1. 1.
    Zhu, Y., Shen, X., Ye, C.: Personalized prediction and sparsity pursuit in latent factor models. J. Am. Stat. Assoc. (2015)Google Scholar
  2. 2.
    Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning (2003)Google Scholar
  3. 3.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent Dirichlet Allocation for tag recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 61–68 (2009)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Pazzani, Michael J., Billsus, Daniel: Content-based recommendation systems. In: Brusilovsky, Peter, Kobsa, Alfred, Nejdl, Wolfgang (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Keerthika, G., Priya, D.S.: Feature subset evaluation and classification using naive Bayes classifier. J. Netw. Commun. Emerg. Technol. (JNCET) 1(2015). www.jncet.org
  8. 8.
    Yannopoulou, N., Moufahim, M., Bian, X.: User-generated brands and social media: couchsurfing and Airbnb. Contemp. Manage. Res. 9 (2013)Google Scholar
  9. 9.
    Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)CrossRefGoogle Scholar
  10. 10.
    Zhang, K., Wang, K., Wang, X., Jin, C., Zhou, A.: Hotel recommendation based on user preference analysis. In: 31st IEEE International Conference on Data Engineering Workshops (ICDEW), pp. 134–138 (2015)Google Scholar
  11. 11.
    Garbers, J., Niemann, M., Mochol, M.: A personalized hotel selection engine. In: Proceedings of the Poster Session of 3rd ESWC (2006)Google Scholar
  12. 12.
    Gutt, D., Herrmann,P.: Sharing Means Caring? Hosts’ Price Reaction to Rating Visibility (2015). aisel.aisnet.org
  13. 13.
    Go, A., Huang, L., Bhayani, R.: Twitter sentiment analysis. Entropy 17 (2009)Google Scholar
  14. 14.
    Yang, D., Yang, A.M.: Classification approach of Chinese texts sentiment based on semantic lexicon and naive Bayesian. Appl. Res. Comput. 27(10), 3737–3739 & 3743 (2010)Google Scholar
  15. 15.
    Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S.: A density-based method for adaptive LDA model selection. Neurocomputing 72(7–9), 1775–1781 (2009)CrossRefGoogle Scholar
  16. 16.
    Cao, J., Zhang, Y.D., Li, J.T., Tang, S.: A method of adaptively selecting best LDA model based on density. Chin. J. Comput. 31(31), 1780–1787 (2008)Google Scholar
  17. 17.
    Wang, Z.Z., Ming, H.E., Du, Y.P.: Text similarity computing based on topic model LDA. Comput. Sci. 40(12), 228–232 (2013)Google Scholar
  18. 18.
    Endres, D.M., Schindelin, J.E.: A new metric for probability distributions. IEEE Trans. Inf. Theory 49(7), 1858–1860 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Liu, S.D., Meng, X.W.: Approach to network services recommendation based on mobile users’ location. J. Softw. 25(11), 2556–2574 (2014)Google Scholar
  20. 20.
    Bell, R.M., Koren, Y.Y.: Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9, 75–79 (2007)CrossRefGoogle Scholar
  21. 21.
    Liang, C.Y., Leng, Y.J.: Collaborative filtering based on information-theoretic co-clustering. Int. J. Syst. Sci. 45(3), 589–597 (2014)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Faculty of Management and EconomicsDalian University of TechnologyDalianPeople’s Republic of China

Personalised recommendations