Advertisement

YVONNE: A Fast and Accurate Prediction Scoring Retrieval Framework Based on MF

  • Yi Yang
  • Caixue Zhou
  • Guangyong Gao
  • Zongmin CuiEmail author
  • Feipeng Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)

Abstract

The recommendation system has many successful applications on e-commerce and social media, including Amazon, Netflix, Yelp, etc. It is a personalized recommendation system. It recommends interesting product and information to the user based on the user’s interests, information, needs, etc. It is extremely important to use the known user information to get the missing information from other users. Most of previous works focus on the learning phase of the recommendation system. Only a few researches focus on the retrieval stage. In this paper, we propose a fast and accurate prediction scoring retrieval framework based on matrix factorization (MF). Our framework (Yvonne) can effectively predict the score of users’ missing items. Experiments with real data show that our framework significantly outperforms other methods on the efficiency and accuracy.

Keywords

Matrix factorization Integral approximate SVD-transformation 

Notes

Acknowledgment

This research was supported by the National Natural Science Foundation of China (Nos. 61762055, 61662039 and 61462048); and the Jiangxi Provincial Natural Science Foundation of China (Nos. 20161BAB202036, 20171BAB202004 and 20181BAB202014).

References

  1. 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)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.C., Wolf, J.L., Wu, K.L., Yu, P.S.: Horting hatches an egg: a new graph-theoretic approach to collaborative filtering, pp. 201–212 (1999)Google Scholar
  3. 3.
    Bachrach, Y., et al.: Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 257–264. ACM (2014)Google Scholar
  4. 4.
    Bell, R.M., Koren, Y.: Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9(2), 75–79 (2007)CrossRefGoogle Scholar
  5. 5.
    Brand, M.: Fast low-rank modifications of the thin singular value decomposition. Linear Algebra Appl. 415, 20–30 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fraccaro, M., Paquet, U., Winther, O.: Indexable probabilistic matrix factorization for maximum inner product search, pp. 1554–1560 (2016)Google Scholar
  7. 7.
    He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: proceedings of the 25th International Conference on World Wide Web. pp. 507–517. International World Wide Web Conferences Steering Committee (2016)Google Scholar
  8. 8.
    Kaltofen, E., May, J.P., Yang, Z., Zhi, L.: Approximate factorization of multivariate polynomials using singular value decomposition. J. Symbolic Comput. 43, 359–376 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Li, H., Chan, T.N., Man, L.Y., Mamoulis, N.: FEXIPRO: fast and exact inner product retrieval in recommender systems. In: ACM International Conference on Management of Data, pp. 835–850 (2017)Google Scholar
  10. 10.
    Li, H., Wu, D., Tang, W., Mamoulis, N.: Overlapping community regularization for rating prediction in social recommender systems, pp. 27–34 (2015)Google Scholar
  11. 11.
    Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 831–840. ACM (2014)Google Scholar
  12. 12.
    Nawrocki, E.P., Kolbe, D.L., Eddy, S.R.: Infernal 1.0: inference of RNA alignments. Bioinformatics 25(10), 1335 (2009)CrossRefGoogle Scholar
  13. 13.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms, pp. 285–295 (2001)Google Scholar
  14. 14.
    Yuan, X., Yang, J.: Sparse and low-rank matrix decomposition via alternating direction methods, vol. 12, p. 2 (2009)Google Scholar
  15. 15.
    Zhu, S., Wu, J., Xiong, H., Xia, G.: Scaling up top-k cosine similarity search. Data Knowl. Eng. 70, 60–83 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yi Yang
    • 1
  • Caixue Zhou
    • 1
  • Guangyong Gao
    • 1
  • Zongmin Cui
    • 1
    Email author
  • Feipeng Wang
    • 1
  1. 1.School of Information Science and TechnologyJiujiang UniversityJiujiangChina

Personalised recommendations