Advertisement

Towards Personalized Context-Aware Recommendation by Mining Context Logs through Topic Models

  • Kuifei Yu
  • Baoxian Zhang
  • Hengshu Zhu
  • Huanhuan Cao
  • Jilei Tian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)

Abstract

The increasing popularity of smart mobile devices and their more and more powerful sensing ability make it possible to capture rich contextual information and personal context-aware preferences of mobile users by user context logs in devices. By leveraging such information, many context-aware services can be provided for mobile users such as personalized context-aware recommendation. However, to the best knowledge of ours, how to mine user context logs for personalized context-aware recommendation is still under-explored. A critical challenge of this problem is that individual user’s historical context logs may be too few to mine their context-aware preferences. To this end, in this paper we propose to mine common context-aware preferences from many users’ context logs through topic models and represent each user’s personal context-aware preferences as a distribution of the mined common context-aware preferences. The experiments on a real-world data set contains 443 mobile users’ historical context data and activity records clearly show the approach is effective and outperform baselines in terms of personalized context-aware recommendation.

Keywords

Personalization Recommender System Context-Aware Mobile Users Latent Dirichlet Allocation (LDA) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Azzopardi, L., Girolami, M., Risjbergen, K.V.: Investigating the relationship between language model perplexity and ir precision-recall measures. In: SIGIR 2003, pp. 369–370 (2003)Google Scholar
  2. 2.
    Bader, R., Neufeld, E., Woerndl, W., Prinz, V.: Context-aware poi recommendations in an automotive scenario using multi-criteria decision making methods. In: CaRR 2011, pp. 23–30 (2011)Google Scholar
  3. 3.
    Bao, T., Cao, H., Chen, E., Tian, J., Xiong, H.: An unsupervised approach to modeling personalized contexts of mobile users. In: ICDM 2010, pp. 38–47 (2010)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Lantent dirichlet allocation. Journal of Machine Learning Research, 993–1022 (2003)Google Scholar
  5. 5.
    Eagle, N., Clauset, A., Quinn, J.A.: Location segmentation, inference and prediction for anticipatory computing. In: AAAI Spring Symposium on Technosocial Predictive Analytics (2009)Google Scholar
  6. 6.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of National Academy of Science of the USA, 5228–5235 (2004)Google Scholar
  7. 7.
    Heinrich, G.: Paramter stimaion for text analysis. Technical report, University of Lipzig (2009)Google Scholar
  8. 8.
    Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR 1999, pp. 50–57 (1999)Google Scholar
  9. 9.
    Jae Kim, K., Ahn, H., Jeong, S.: Context-aware recommender systems using data mining techniques. Journal of World Academy of Science, Engineering and Technology 64, 357–362 (2010)Google Scholar
  10. 10.
    Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: RecSys 2010, pp. 79–86 (2010)Google Scholar
  11. 11.
    Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39, 103–134 (2000)zbMATHCrossRefGoogle Scholar
  12. 12.
    Park, M.-H., Hong, J.-H., Cho, S.-B.: Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices. In: Indulska, J., Ma, J., Yang, L.T., Ungerer, T., Cao, J. (eds.) UIC 2007. LNCS, vol. 4611, pp. 1130–1139. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Sohn, T., Li, K.A., Lee, G., Smith, I., Scott, J., Griswold, W.G.: Place-Its: A Study of Location-Based Reminders on Mobile Phones. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 232–250. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Tung, H.-W., Soo, V.-W.: A personalized restaurant recommender agent for mobile e-service. In: EEE 2004, pp. 259–262 (2004)Google Scholar
  15. 15.
    van Setten, M., Pokraev, S., Koolwaaij, J.: Context-Aware Recommendations in the Mobile Tourist Application COMPASS. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 235–244. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Woerndl, W., Schueller, C., Wojtech, R.: A hybrid recommender system for context-aware recommendations of mobile applications. In: ICDE 2007, pp. 871–878 (2007)Google Scholar
  17. 17.
    Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: AAAI 2010, pp. 236–241 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kuifei Yu
    • 1
    • 2
  • Baoxian Zhang
    • 1
  • Hengshu Zhu
    • 2
    • 3
  • Huanhuan Cao
    • 2
  • Jilei Tian
    • 2
  1. 1.Graduate University of Chinese Academy of SciencesChina
  2. 2.Nokia Research CenterChina
  3. 3.University of Science and Technology of ChinaChina

Personalised recommendations