Advertisement

The Review of Recommendation System

  • Ning WangEmail author
  • Hui Zhao
  • Xue Zhu
  • Nan Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

With the development of the Internet, the amount of information continues to increase, and the problem of “information overloading” is becoming more and more obvious. Simple information retrieval can no longer satisfies the needs of users to search for accurate information, and the recommendation system emerges. Although the recommendation system is widely used in e-commerce, the recommended algorithm faces more difficulties. The paper firstly introduces the related concepts, the directions of application and the principles of the recommendation system, then the paper analyzes the advantages and disadvantages of these algorithms. Finally, it summarizes some main problems and the directions of the research the recommendation system needs to solve.

Keywords

Recommendation system Cold start Sparse problems predict 

Notes

Acknowledgement

The work was supported by the education department of hebei province (NO. QN2016142, YQ2014014) and the natural science foundation of hebei province (NO. F2015402119). Thanks to my teachers for guidance and the help of my classmates.

References

  1. 1.
    Wang, Y.K., Cheng, Q.: Summary and development trends of relevance research on information retrieval (01), 88–94 (2012). ooks & InformationGoogle Scholar
  2. 2.
    Goldberg, D., Nichols, D., Oki, B.M., et al.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  3. 3.
    Adomavicius, G., Tuzhilin, A.: Towards 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
  4. 4.
    Xu, H.L., Wu, X., Li, X.D., Yan, B.P.: Comparison study of Internet recommendation system. Ruanjian Xuebao J. Softw. 20(2), 350–362 (2009)CrossRefGoogle Scholar
  5. 5.
    Jannach, D., Zanker, M.: Recommender Systems, pp. 1–5. Posts and Telecom Press (2013)Google Scholar
  6. 6.
    Wang, L.C., Meng, X.W., Zhang, Y.J.: Context-aware recommender systems. Ruanjian Xuebao J. Softw. 23(1), 1–20 (2012)CrossRefGoogle Scholar
  7. 7.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  8. 8.
    Zenebe, A., Norcio, A.F.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst. 160(1), 76–94 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5(1/2), 115–153 (2001)CrossRefGoogle Scholar
  10. 10.
    Li, C.: Research on the bottleneck problems of collaborative filtering in E-commerce recommender systems. Ph.D Dissertation. Hefei University of Technology, Hefei, China (2009)Google Scholar
  11. 11.
    Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, Hong Kong, China, pp. 285–295 (2001)Google Scholar
  12. 12.
    Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Proceedings of the 3rd International Conference on Trust Management, Paris, France, pp. 224–239 (2005) Google Scholar
  13. 13.
    Liu, W.: Research on information recommendation methods in E-commerce system. Inf. Sci. 24(2), 300–303 (2006)Google Scholar
  14. 14.
    Wise, J.A., et al.: Visualizing the non-visual: spatial analysis and interaction with information from text documents. In: IEEE Information Visualization 1995, pp. 30–31. IEEE Computer Society Press (1995)Google Scholar
  15. 15.
    Golub, G., Kahan, K.: Calculating the singular values and pseudo-inverse of a matrix. J. Soc. Ind. Appl. Math. 2(2), 205–224 (1965)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2000)Google Scholar
  17. 17.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, vol. 20, no. 3 (2008)Google Scholar
  18. 18.
    Liu, P., Nie, P., Chen, D.: Design of knowledge-based E-commerce intelligent recommendation system platform. Comput. Eng. Appl. (19), 199–201+216 (2007)Google Scholar
  19. 19.
    Liu, Z.: Research on content-based social label recommendation technology. Harbin Engineering University (2012)Google Scholar
  20. 20.
    Beel, J., Gipp, B., Langer, S., et al.: Paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)CrossRefGoogle Scholar
  21. 21.
    Yu, L., Liu, L., Li, X.F.: A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-commerce. Expert Syst. Appl. 28(1), 67–77 (2005)CrossRefGoogle Scholar
  22. 22.
    Wang, Z., Liu, Y., Yang, J., et al.: A personalization-oriented academic literature recommendation method. Data Sci. J. 4, 1–9 (2015)Google Scholar
  23. 23.
    Younus, A., Qureshi, M.A., Manchanda, P., O’Riordan, C., Pasi, G.: Utilizing microblog data in a topic modelling framework for scientific articles’ recommendation. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 384–395. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-13734-6_28CrossRefGoogle Scholar
  24. 24.
    Marmanis, H., Babenko, D.: Web 智能算法.阿稳.陈刚等.电子工业出版社, pp. 73–117 (2011)Google Scholar
  25. 25.
    Maes, P.: Agents that reduce work and information overload. Commun. ACM 37(7), 30–40 (1994)CrossRefGoogle Scholar
  26. 26.
    Su, J.H., Wang, B.W., Hsiao, C.Y., et al.: Personalized rough-set-based recommendation by integrating multiple contents and collaborative information. Inf. Sci. 180(1), 113–131 (2010)CrossRefGoogle Scholar
  27. 27.
    Chen, Q.: Research on collaborative filtering recommendation algorithm based on SVD. Southwest Jiaotong University, pp. 26–28 (2015)Google Scholar
  28. 28.
    Guo, X.: Research on microblog user tag recommendation algorithm. Anhui University, pp. 19–20 (2018)Google Scholar
  29. 29.
    Felfernig, A., Friedrich, G., Jannach, D., et al.: An integrated environment for the development of knowledge-based recommender applications. Int. J. Electron. Commer. 11(2), 11–34 (2006)CrossRefGoogle Scholar
  30. 30.
    Felfernig, A., Burke, R.: Constraint-based recommender systems: technologies and research issues. In: ACM International Conference Proceeding Series. ACM, New York (2008)Google Scholar
  31. 31.
    Zanker, M., Jessenitschnig, M., Schmid, W.: Preference reasoning with soft constraints in constraint-based recommender systems. Constraints 15(4), 574–595 (2010)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Burke, R.: Knowledge-based recommender systems. Encycl. Libr. Inf. Sci. 69(Suppl. 32), 180–200 (2000)Google Scholar
  33. 33.
    Bridge, D., Göker, M.H., Mcginty, L., et al.: Case-based recommender systems. Knowl. Eng. Rev. 20(3), 315–320 (2005)CrossRefGoogle Scholar
  34. 34.
    Hua, Y.: Research on product recommendation algorithm based on graph. Jiangxi Normal University, pp. 10–15 (2017)Google Scholar
  35. 35.
    Lee, K., Lee, K.: Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst. Appl. 42(10), 4851–4858 (2015)CrossRefGoogle Scholar
  36. 36.
    Lien, D.T., Anh, N.X., Phuong, N.D.: A graph model for hybrid recommender system. In: Proceedings of the 7th International Conference on Knowledge and Systems Engineering, pp. 138–143. IEEE, Washington (2015)Google Scholar
  37. 37.
    Albadvi, A., Shahbazi, M.: A hybrid recommendation technique based on product category attributes. Expert Syst. Appl. 36(9), 11480–11488 (2009)CrossRefGoogle Scholar
  38. 38.
    Barragáns-Martínez, A.B., Costa-Montenegro, E., Burguillo-Rial, J.C., et al.: A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf. Sci. 180(22), 4290–4311 (2010)CrossRefGoogle Scholar
  39. 39.
    Kim, H.N., Ji, A.T., Ha, I., et al.: Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron. Commer. Res. Appl. 9(1), 73–83 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Hebei University of EngineeringHandanChina

Personalised recommendations