International Conference on Collaborative Computing: Networking, Applications and Worksharing

Collaborative Computing: Networking, Applications, and Worksharing pp 296-302 | Cite as

LTMF: Local-Based Tag Integration Model for Recommendation

  • Deyuan Zheng
  • Huan Huo
  • Shang-ye Chen
  • Biao Xu
  • Liang Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 163)

Abstract

There are two primary approaches to collaborative filtering: memory- based and model-based. The traditional techniques fail to integrate with these two approaches and also can’t fully utilize the tag features which data contains. Based on mining local information, this paper combines neighborhood method and matrix factorization technique. By taking fuller consideration of the tag features, we propose an algorithm named LTMF (Local-Tag MF). After the real data validation, this model performs better than other state-of-art algorithms.

References

  1. 1.
    Kim, B.S., Kim, H., Lee, J., Lee, J.-H.: Improving a recommender system by collective matrix factorization with tag information. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS), and 15th International Symposium on Advanced Intelligent Systems (ISIS), pp. 980–984. IEEE (2014)Google Scholar
  2. 2.
    Grivolla, J., Badia, T., Campo, D., Sonsona, M., Pulido, J.-M.: A hybrid recommender combining user, item and interaction data. In: 2014 International Conference on Computational Science and Computational Intelligence (CSCI), vol. 1, pp. 297–301. IEEE (2014)Google Scholar
  3. 3.
    Si, L., Jin, R.: Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 156–157. ACM (2004)Google Scholar
  4. 4.
    Wang, J., De Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508. ACM (2006)Google Scholar
  5. 5.
    Insuwan, W., Suksawatchon, U., Suksawatchon, J.: Improving missing values imputation in collaborative filtering with user-preference genre and singular value decomposition. In: 2014 6th International Conference on Knowledge and Smart Technology (KST), pp. 87–92. IEEE (2014)Google Scholar
  6. 6.
    Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: 2007 Seventh IEEE International Conference on Data Mining ICDM 2007, pp. 43–52. IEEE (2007)Google Scholar
  7. 7.
    Funk, S.: Netflix update: Try this at home, December 2006Google Scholar
  8. 8.
    Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2007)Google Scholar
  9. 9.
    Chatti, M.A., Dakova, S., Thus, H., Schroeder, U.: Tag-based collaborative filtering recommendation in personal learning environments. IEEE Trans. Learn. Technol. 6(4), 337–349 (2013)CrossRefGoogle Scholar
  10. 10.
    Pirasteh, P., Jung, J.J., Hwang, D.: Item-based collaborative filtering with attribute correlation: a case study on movie recommendation. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014, Part II. LNCS, vol. 8398, pp. 245–252. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, X., Xu, C.: SBMF: Similarity-based matrix factorization for collaborative recommendation. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 379–383. IEEE (2014)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Deyuan Zheng
    • 1
  • Huan Huo
    • 1
  • Shang-ye Chen
    • 2
  • Biao Xu
    • 1
  • Liang Liu
    • 1
  1. 1.University of Shanghai for Science and TechnologyShanghaiChina
  2. 2.School of Information and TechnologyNorthwest UniversityXianChina

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