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Collaborative Recommender System (CRS) Using Optimized SGD - ALS

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1440))

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Abstract

Matrix factorization (MF), dimensional reduction techniques are broadly used in recommender systems (RS) to retrieve the preference of user from explicit ratings. However, the interactions are not always consistent due to the influence of numerous elements on users on a product, including friend’s recommendation and business publicizing. In comparison, traditional MF is not able to find consistent ratings. Find the exact prediction/ratings of a product/item is essential for further improvement of the performance of the collaborative recommender framework. To find the exact prediction, we propose the parameter optimizing stochastic gradient descent (SGD) and alternate least square (ALS) over MF. Furthermore, we examine the deviation of prediction error after setting each parameter over a general parameter distribution of both techniques (SGD and ALS). To evaluate the performance of the proposed model, we use two well-known datasets. The exploratory outcomes reveal that our approach gets significant improvement over the base model.

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Notes

  1. 1.

    https://grouplens.org/datasets/movielens/100k/.

References

  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)

    Article  Google Scholar 

  2. Behera, G., Bhoi, A.K., Bhoi, A.: UHWSF: univariate holt winter’s based store sales forecasting. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds.) Intelligent Systems. LNNS, vol. 185, pp. 283–292. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6081-5_25

  3. Behera, G., Nain, N.: A comparative study of big mart sales prediction. In: Nain, N., Vipparthi, S.K., Raman, B. (eds.) CVIP 2019. CCIS, vol. 1147, pp. 421–432. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4015-8_37

    Chapter  Google Scholar 

  4. Behera, G., Nain, N.: Grid search optimization (GSO) based future sales prediction for big mart. In: 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 172–178. IEEE (2019)

    Google Scholar 

  5. Cacheda, F., Carneiro, V., Fernández, D., Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web (TWEB) 5(1), 1–33 (2011)

    Article  Google Scholar 

  6. Cai, Y., Leung, H.f., Li, Q., Min, H., Tang, J., Li, J.: Typicality-based collaborative filtering recommendation. IEEE Trans. Knowl. Data Eng. 26(3), 766–779 (2013)

    Google Scholar 

  7. Chen, L., De Gemmis, M., Felfernig, A., Lops, P., Ricci, F., Semeraro, G.: Human decision making and recommender systems. ACM Trans. Interactive Intell. Syst. (TiiS) 3(3), 1–7 (2013)

    Article  Google Scholar 

  8. Funk, S.: Netflix update: Try this at home (2006)

    Google Scholar 

  9. He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 549–558 (2016)

    Google Scholar 

  10. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)

    Article  Google Scholar 

  11. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272. IEEE (2008)

    Google Scholar 

  12. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 22(1), 116–142 (2004)

    Article  Google Scholar 

  13. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)

    Google Scholar 

  14. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  15. Li, N., Li, C.: Zero-sum reward and punishment collaborative filtering recommendation algorithm. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 548–551. IEEE (2009)

    Google Scholar 

  16. Mavridis, A.: Matrix factorization techniques for recommender systems (2017)

    Google Scholar 

  17. Mehta, R., Rana, K.: A review on matrix factorization techniques in recommender systems. In: 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), pp. 269–274. IEEE (2017)

    Google Scholar 

  18. Meng, J., Zheng, Z., Tao, G., Liu, X.: User-specific rating prediction for mobile applications via weight-based matrix factorization. In: 2016 IEEE International Conference on Web Services (ICWS), pp. 728–731. IEEE (2016)

    Google Scholar 

  19. Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and Workshop, vol. 2007, pp. 5–8 (2007)

    Google Scholar 

  20. Patra, B.K., Launonen, R., Ollikainen, V., Nandi, S.: A new similarity measure using bhattacharyya coefficient for collaborative filtering in sparse data. Knowl.-Based Syst. 82, 163–177 (2015)

    Article  Google Scholar 

  21. Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Recommender Systems Handbook, pp. 1–34. Springer (2015)

    Google Scholar 

  22. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth International Conference on Computer and Information Science, vol. 1, pp. 27–8. Citeseer (2002)

    Google Scholar 

  23. Xue, W., Xiao, B., Mu, L.: Intelligent mining on purchase information and recommendation system for e-commerce. In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 611–615. IEEE (2015)

    Google Scholar 

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Authors and Affiliations

  1. Department Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

    Gopal Behera & Neeta Nain

Authors
  1. Gopal Behera
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  2. Neeta Nain
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Corresponding author

Correspondence to Gopal Behera .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. MVPS's Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, Maharashtra, India

    V. R. Sonawane

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Behera, G., Nain, N. (2021). Collaborative Recommender System (CRS) Using Optimized SGD - ALS. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_55

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  • DOI: https://doi.org/10.1007/978-3-030-81462-5_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81461-8

  • Online ISBN: 978-3-030-81462-5

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