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MLE: A General Multi-Layer Ensemble Framework for Group Recommendation

  • Xiaopeng Li
  • Jia Xu
  • Bin XiaEmail author
  • Jian Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)

Abstract

As the number of users and locations has increased dramatically in location-based social networks, it becomes a big challenge to recommend point-of-interests (POIs) meeting users’ preference. In traditional recommendation tasks, personalized recommendations performs well, however, these methods also have many disadvantages such as the long-tailed problem and the strong assumption. Further, in general scenarios, a group of users (e.g., colleagues, friends, and family members) often visit a specific location to enjoy time together (e.g., meal and shopping). Thus, it is more meaningful to recommend locations to the group than to individuals. However, the existing group recommendation approaches also have some limitations that hardly capture the preferences of a group of users effectively. To make full use of the users’ preferences and improve the effectiveness of group recommendation, in this paper, we propose a multi-layer ensemble framework which has a two-step fusion process. For the first step, we employ several personalized recommendation methods to generate the recommendations for individuals, and the recommendation list is obtained using the proposed fusion approach based on the supervised learning. For the second step, we utilize several ranking aggregation algorithms to fuse the recommendations list of individuals in the group and propose an unsupervised learning based ranking algorithm (URank) to further fuse the results of ranking aggregations to obtain the final group recommendation list. The experiments are conducted on a real-world dataset, and the results demonstrate the effectiveness of our proposed general framework.

Keywords

Group recommendation Ranking aggregation Unsupervised learning General ensemble model 

Notes

Acknowledgments

The work was supported in part by the National Natural Science Foundation of China (Grant No. 61472193, No. 61872193, No. 61802205 and No. 61872186), the Natural Science Research Project of Jiangsu Province under Grant 18KJB520037, and the research funds of NJUPT under Grant NY218116.

References

  1. 1.
    Argentini, A.: Ranking aggregation based on belief function theory. Ph.D. thesis, University of Trento (2012)Google Scholar
  2. 2.
    Burnham, K.P., Anderson, D.R.: Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York (2003).  https://doi.org/10.1007/b97636CrossRefzbMATHGoogle Scholar
  3. 3.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation revisited (2001)Google Scholar
  4. 4.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, pp. 613–622. ACM (2001)Google Scholar
  5. 5.
    Fagin, R., Kumar, R., Sivakumar, D.: Efficient similarity search and classification via rank aggregation. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 301–312. ACM (2003)Google Scholar
  6. 6.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66(4), 614–656 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  8. 8.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  9. 9.
    Liu, Z., Li, T., Wang, J.: A survey on event mining for ict network infrastructure management. ZTE Commun. 14(2), 47–55 (2016)Google Scholar
  10. 10.
    Liu, Z., Li, T., Zhou, Q.: Application driven big data mining. In: ZTE Technology, pp. 49–52 (2016)Google Scholar
  11. 11.
    McKenzie, T.G., et al.: Novel models and ensemble techniques to discriminate favorite items from unrated ones for personalized music recommendation. In: Proceedings of the 2011 International Conference on KDD Cup 2011, vol. 18, pp. 101–135. JMLR.org (2011)Google Scholar
  12. 12.
    Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)Google Scholar
  13. 13.
    Pihur, V., Datta, S., Datta, S.: Rankaggreg, an R package for weighted rank aggregation. BMC Bioinform. 10(1), 62 (2009)CrossRefGoogle Scholar
  14. 14.
    Piotte, M., Chabbert, M.: The pragmatic theory solution to the Netflix grand prize. Netflix Prize Documentation (2009)Google Scholar
  15. 15.
    Qin, T., Geng, X., Liu, T.Y.: A new probabilistic model for rank aggregation. In: Advances in Neural Information Processing Systems, pp. 1948–1956 (2010)Google Scholar
  16. 16.
    Qin, T., Liu, T.Y., Li, H.: A general approximation framework for direct optimization of information retrieval measures. Inf. Retrieval 13(4), 375–397 (2010)CrossRefGoogle Scholar
  17. 17.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)Google Scholar
  18. 18.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887. ACM (2008)Google Scholar
  19. 19.
    Xia, B., Li, T., Li, Q., Zhang, H.: Noise-tolerance matrix completion for location recommendation. Data Mining Knowl. Discov. 32(1), 1–24 (2018)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Xia, B., Li, T., Zhou, Q.F., Li, Q., Zhang, H.: An effective classification-based framework for predicting cloud capacity demand in cloud services. IEEE Trans. Serv. Comput. (2018)Google Scholar
  21. 21.
    Xia, B., Li, Y., Li, Q., Li, T.: Attention-based recurrent neural network for location recommendation. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1–6. IEEE (2017)Google Scholar
  22. 22.
    Xia, B., Ni, Z., Li, T., Li, Q., Zhou, Q.: Vrer: context-based venue recommendation using embedded space ranking svm in location-based social network. Expert Syst. Appl. 83, 18–29 (2017)CrossRefGoogle Scholar
  23. 23.
    Xu, J., Li, H., Li, Y., Yang, D., Li, T.: Incentivizing the biased requesters: truthful task assignment mechanisms in crowdsourcing. In: 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE (2017)Google Scholar
  24. 24.
    Xu, J., Rao, Z., Xu, L., Yang, D., Li, T.: Mobile crowd sensing via online communities: incentive mechanisms for multiple cooperative tasks. In: IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 171–179. IEEE (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Big Data Security and Intelligent ProcessingNanjing University of Posts and TelecommunicationsNanjingPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina

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