Skip to main content
SpringerLink
Log in

Improved FC-LFM Algorithm Integrating Time Decay Factor

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Different from the search engine system, the recommendation system can help users quickly find their interesting items from the massive data in a personalized way. Traditional collaborative filtering algorithms based on users and items need to define the granularity, dimension, and weight of classification subjectively when calculating similarity based on user behavior. So the accuracy and calculation efficiency of prediction scoring results are not high enough. LFM (latent factor model) based on data itself, adopts automatic clustering according to user behavior and uses a machine-learning method to mine hidden features from the user’s historical scoring data. But when the amount of data is large, there exists data sparsity in the user rating matrix. Also, the user’s interest is always changing with time, and the items themselves have a certain life cycle. Based on the above problems, this paper first proves that the accuracy of LFM model is influenced by the popularity and diversity of negative samples through comparative experiments. Then, a new algorithm called FC-LFM (forgetting curve-latent factor model) is proposed. In this algorithm, the Ebbinghaus forgetting curve function is introduced to improve LFM model and the time decay factor is integrated into the iterative operation of negative sample popularity, matrix filling, user feature matrix, and item feature matrix. In the end, the improved FC-LFM collaborative filtering algorithm is proved to be superior to the traditional UserCF, UserCF-IIF, ItemCF, ItemCF-IUF, and LFM algorithm in accuracy and recall rate by comparing experiments on the MovieLens data set.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Jones, S.L.; Kelly, R.: Dealing with information overload in multifaceted personal informatics systems. Human Comput. Interact. 33, 1–48 (2018)

    Article  Google Scholar 

  2. Yue-Qiang, R.; Ze, W.; Xiao-Na, S.; Shi-Min, S.: A multi-element hybrid location recommendation algorithm for location based social networks. IEEE Access 7, 100416–100427 (2019)

    Article  Google Scholar 

  3. Hou, D.; Li, G.; Chen, D.; Zhu, B.; Hu, S.: Evaluation and analysis on the green development of China’s industrial parks using the long-tail effect model. J. Environ. Manag. 248, 109288 (2019)

    Article  Google Scholar 

  4. Parwita, W.G.S.; Swari, M.H.P.; Welda, W.: Perancangan sistem rekomendasi dokumen dengan pendekatan content-based filtering. Comput. Eng. Sci. Syst. J. 3, 65–74 (2018)

    Google Scholar 

  5. Maheswari, M.; Geetha, S.: Others: adaptable and proficient hellinger coefficient based collaborative filtering for recommendation system. Clust. Comput. 22, 12325–12338 (2019)

    Article  Google Scholar 

  6. Yuan, Y.; Luo, X.; Shang, M.: Effects of preprocessing and training biases in latent factor models for recommender systems. Neurocomputing 275, 2019–2030 (2018)

    Article  Google Scholar 

  7. Yi N.; Li C.; Feng X.; Shi M.: Design and implementation of movie recommender system based on graph database. In: 2017 14th Web Information Systems and Applications Conference (WISA), IEEE, pp. 132-135 (2017)

  8. Cui B.: Design and implementation of movie recommendation system based on Knn collaborative filtering algorithm. In: ITM Web of Conferences. 4008. EDP Sciences, (2017)

  9. Wu Y.; Li Y.; Qian R.: NE-UserCF: Collaborative filtering recommender system model based on NMF and E\(^2\)LSH. Int. J. Perform. Eng. 13, (2017)

  10. Cui, Z.; et al.: Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Trans. Serv. Comput. (2020)

  11. Iwendi, C.; et al.: Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model. IEEE Access 8, 28462–28474 (2020)

    Article  Google Scholar 

  12. Nassar, N.; Jafar, A.; Rahhal, Y.: A novel deep multi-criteria collaborative filtering model for recommendation system. Knowl. Based Syst. 187, 104811 (2020)

    Article  Google Scholar 

  13. Mondal, S.; Basu, A.; Mukherjee, N.:Building a trust-based doctor recommendation system on top of multilayer graph database. J. Biome.d Inform. p. 103549 (2020).

  14. Movahedian, H.; Khayyambashi, M.R.: Folksonomy-based user interest and disinterest profiling for improved recommendations: an ontological approach. J. Inf. Sci. 40, 594–610 (2014)

    Article  Google Scholar 

  15. Xia, B.; Li, T.; Li, Q.; Zhang, H.: Noise-tolerance matrix completion for location recommendation. Data Min. Knowl. Dis. 32, 1–24 (2018)

    Article  MathSciNet  Google Scholar 

  16. Yang, N.; Ma, Y.; Chen, L.; Philip, S.Y.: A meta-feature based unified framework for both cold-start and warm-start explainable recommendations. World Wide Web. pp. 1–25, (2019)

  17. Liu, J.: Study and implementation of personalized news recommendation system based on topic models. Beijing University of Posts and Telecommunications, Beijing (2013)

    Google Scholar 

  18. Rodas-Silva J.; Galindo J.E.A.; Garc I A-Guti E Rrez J.; Benavides D.: Selection of software product line implementation components using recommender systems: an application to wordpress, IEEE Access (2019)

  19. Guo, Y.: Research on personalized recommendation strategy in mobile E-commerce. Chengdu University of Technology, Chengdu (2018)

    Google Scholar 

  20. Gong, C.: Research on dynamic collaborative filtering algorithm based on latent semantic model. Beijing University of Technology, Beijing (2018)

    Google Scholar 

  21. Bao S.; Xu Q.; Ma K.; Yang Z.; Cao X.; Huang Q.: Collaborative Preference Embedding against Sparse Labels. In: Proceedings of the 27th ACM International Conference on Multimedia, ACM. pp. 2079-2087 (2019)

  22. He, T.: Image classification based on sparse coding multi-scale spatial latent semantic analysis. EURASIP J. Image Video Process. 2019, 38 (2019)

    Article  Google Scholar 

  23. English J.A.; Kossarian M.M.; McManis C.E.; Smith D.A.; Others: phenomenological semantic distance from latent dirichlet allocations (LDA) classification (2019)

  24. Yao Z.; Wang J.; Han Y.: An improved neighborhood-based recommendation algorithm optimization with clustering analysis and latent factor model. In: 2019 Chinese Control Conference (CCC), IEEE, pp. 3744-3748 (2019)

  25. Chun-He, Du.: The improvement and implementation of latent factor model recommendation algorithm in big data environment. South China University of Technology, GuangZhou (2017)

    Google Scholar 

  26. Gao, H.A.T.J.: Addressing the cold-start problem in location recommendation using geo-social correlations. Data Min. Knowl. Dis. 29, 299–323 (2015)

    Article  Google Scholar 

  27. Ji, K.; Shen, H.: Addressing cold-start: scalable recommendation with tags and keywords. Knowl. Based Syst. 83, 42–50 (2015)

    Article  Google Scholar 

  28. Wang, P.; Li, L.; Gan, J.: PLSA collaborative filtering algorithm based on user interest change. J. Yunnan Normal Univ. Nat. Sci. Edn. 37, 39–43 (2017)

    Google Scholar 

  29. Xu, J.; Li, X.; Chen, H.; Hao, X.: A recommendation algorithm of latent factor model based on user interest migration. Wireless Communication Technology 7, (2019)

  30. Yu-fang, Z.; Jin-long, D.; Zhong-yang, X.: Collaborative filtering algorithm based on two-step filling foralleviating data sparsity. Appl. Res. Comput. 30, 2602–2605 (2013)

    Google Scholar 

  31. Ding, S.; Dong-hong, J.; Wang, L.: Collaborative filtering recommendation algorithm based on user attributes and scores. Comput. Eng. Des. 36, 487–491 (2015)

    Google Scholar 

  32. Zhang, Q.; Yu, B.; Wang, H.; Deng, L.: An efficient recommendation algorithm for relieving data sparsity in collaborative filtering. J. Hefei Univ. Technol. Nat. Sci. 42, 473–478 (2019)

    Google Scholar 

  33. Wang, E.: Research and implementation of E-commerce personalized recommendation system based on latent factor model and clustering algorithm. Beijing University of Posts and Telecommunications, Beijing (2017)

    Google Scholar 

  34. Xiang, L.: Recommend system practice. The People’s Posts and Telecommunications Press (2012)

  35. Shu, J.; Shen, X.; Liu, H.; Yi, B.; Zhang, Z.: A content-based recommendation algorithm for learning resources. Multimedia Syst. 24, 163–173 (2018)

    Article  Google Scholar 

  36. Rutkowski T.; Romanowski J.; Woldan P.; Staszewski P.L.; Nielek R.L.A.; Rutkowski L.: A content-based recommendation system using neuro-fuzzy approach. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, pp. 1-8 (2018)

  37. Ramzan B.; Bajwa I.S.; Jamil N.; Mirza F.: An intelligent data analysis for hotel recommendation systems using machine learning,arXiv preprint arXiv:1910.06669,(2019)

  38. Zamani Boroujeni, F..A..B..M.: Improving collaborative recommendations using vector quantization and clustering. Social Netw. Anal. Min. 6, 71 (2016)

    Article  Google Scholar 

  39. Bauer C.; Schedl M.: A cross-country investigation of user connection patterns in online social networks. In: Proceedings of the 52nd Hawaii International Conference on System Sciences (2019)

  40. Han, J.; Zheng, L.; Huang, H.; Xu, Y.; Philip, S.Y.; Zuo, W.: deep latent factor model with hierarchical similarity measure for recommender systems. Inf. Sci. 503, 521–532 (2019)

    Article  Google Scholar 

  41. Zhang, L.; Liu, P.; Gulla, J.A.: Dynamic attention-integrated neural network for session-based news recommendation. Machine Learning, pp 1–25 (2019)

  42. Sun, B.; Dong, L.: Dynamic model adaptive to user interest drift based on cluster and nearest neighbors. IEEE Access 5, 1682–1691 (2017)

    Article  Google Scholar 

  43. Lakshmi, T.J.; Bhavani, S.D.: Temporal probabilistic measure for link prediction in collaborative networks. Appl. Intell. 47, 83–95 (2017)

    Article  Google Scholar 

  44. Song Y.; Elkahky A.M.; He X.: Multi-rate deep learning for temporal recommendation. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. pp. 909-912. ACM (2016)

  45. Lai, C.; Lee, S.; Huang, H.: A social recommendation method based on the integration of social relationship and product popularity. Int. J. HumanComput. Stud. 121, 42–57 (2019)

    Article  Google Scholar 

  46. Shen, J.; Cheng, Z.; Yang, M.; Han, B.; Li, S.: Style-oriented personalized landmark recommendation. IEEE Trans. Ind. Electron. (2019)

  47. Friedman, J.; Hastie, T.; Tibshirani, R.: The elements of statistical learning. Springer series in statistics, New York (2001)

    MATH  Google Scholar 

  48. Jin C.; Netrapalli P.; Ge R.; Kakade S.M.; Jordan M.I.: Stochastic gradient descent escapes saddle points efficiently, arXiv preprint arXiv:1902.04811,2019

  49. Laboratory G.R.: MovieLens 100K dataset. https://grouplens.org/datasets/movielens/100k/(1998). Accessed 20 Nov 2019

Download references

Acknowledgements

Funding by the Science and Technology Planning Project of Zhejiang Province (Grant No.2018C01084), the Public Welfare Project Foundation of Zhejiang Provincial Science and Technology Department (Grant No. LGG18F020006), the Foundation of Zhejiang Provincial Education Department(Grant No. Y201737672), the Natural Science Foundation of China(61871468), the Zhejiang Provincial Natural Science Foundation of China (LY18F010006) is gratefully acknowledged.

Author information

Authors and Affiliations

  1. College of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China

    Gan Zhi-Gang, Jiang Xiao-Ning, Yu Chang-Hong, Yu Bin-Xiao & Li Chuan-Huang

  2. Engineering and Design, University of Sussex, Brighton, BN1 9RH, UK

    Gan Zhi-Gang

  3. College of Intelligent Transportation, Zhejiang Institute of Communications, Hangzhou, 311112, China

    Rao Shen

Authors
  1. Gan Zhi-Gang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rao Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiang Xiao-Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Chang-Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Bin-Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Chuan-Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gan Zhi-Gang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhi-Gang, G., Shen, R., Xiao-Ning, J. et al. Improved FC-LFM Algorithm Integrating Time Decay Factor. Arab J Sci Eng 46, 8629–8639 (2021). https://doi.org/10.1007/s13369-021-05637-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05637-0

Keywords

Search

Navigation