A new curve fitting based rating prediction algorithm for recommender systems

Yilmaz Ar; Şahin Emrah Amrahov; Nizami A. Gasilov; Sevgi Yigit-Sert

Kybernetika (2022)

  • Volume: 58, Issue: 3, page 440-455
  • ISSN: 0023-5954

Abstract

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The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase the accuracy of rating prediction by using additional information. However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction. In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items. The proposed algorithm is easy to implement. The main advantage of the algorithm is that the running time is polynomial, namely it is θ ( n 2 ) , for sparse matrices. Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms.

How to cite

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Ar, Yilmaz, et al. "A new curve fitting based rating prediction algorithm for recommender systems." Kybernetika 58.3 (2022): 440-455. <http://eudml.org/doc/298912>.

@article{Ar2022,
abstract = {The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase the accuracy of rating prediction by using additional information. However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction. In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items. The proposed algorithm is easy to implement. The main advantage of the algorithm is that the running time is polynomial, namely it is $\theta (n^2)$, for sparse matrices. Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms.},
author = {Ar, Yilmaz, Emrah Amrahov, Şahin, Gasilov, Nizami A., Yigit-Sert, Sevgi},
journal = {Kybernetika},
keywords = {recommender systems; collaborative filtering; curve fitting},
language = {eng},
number = {3},
pages = {440-455},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A new curve fitting based rating prediction algorithm for recommender systems},
url = {http://eudml.org/doc/298912},
volume = {58},
year = {2022},
}

TY - JOUR
AU - Ar, Yilmaz
AU - Emrah Amrahov, Şahin
AU - Gasilov, Nizami A.
AU - Yigit-Sert, Sevgi
TI - A new curve fitting based rating prediction algorithm for recommender systems
JO - Kybernetika
PY - 2022
PB - Institute of Information Theory and Automation AS CR
VL - 58
IS - 3
SP - 440
EP - 455
AB - The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase the accuracy of rating prediction by using additional information. However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction. In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items. The proposed algorithm is easy to implement. The main advantage of the algorithm is that the running time is polynomial, namely it is $\theta (n^2)$, for sparse matrices. Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms.
LA - eng
KW - recommender systems; collaborative filtering; curve fitting
UR - http://eudml.org/doc/298912
ER -

References

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  1. Acilar, A. M., Arslan, A., , Expert Systems with Applications 36 (2009), 8324-8332. DOI
  2. Alhijawi, B., Awajan, A., , In: 6th International Congress on Information and Communication Technology, Singapore 2022, pp. 105-116. DOI
  3. Al-Shamri, M. Y. H., , Expert Systems Appl. 41 (2014), 5680-5688. DOI
  4. Ar, Y., , Evolution. Intell. 13 (2020), 269-281. DOI
  5. Ar, Y., , Expert Systems Appl. 41 (2016), 122-128. DOI
  6. Bobadilla, J., Ortega, F., Hernando, A., Bernal, J., , Expert Systems Appl. 39 (2012), 172-186. DOI
  7. Bokde, D., Girase, S., Mukhopadhyay, D., , Procedia Computer Sci. 49 (2015), 136-146. DOI
  8. Chen, J., Zhao, C., Chen, L., , Complex Intell. Systems 6 (2020), 147-156. DOI
  9. Christakopoulou, E., Karypis, G., , In: Advances in Knowledge Discovery and Data Mining, Taiwan 2014, pp. 38-49. DOI
  10. Cornelis, C., Lu, J., Guo, X., Zhang, G., , Inform. Sci. 177 (2007), 4906-4921. DOI
  11. Maio, C. De, Fenza, G., Gaeta, M., Loia, V., Orciuoli, F., Senatore, S., , Applied Soft Computing 12 (2012), 1, 113-124. DOI
  12. Meo, P. De, Ferrara, E., Fiumara, G., Provetti, A., , In: 11th International Conference on Intelligent Systems Design and Applications 2011, pp. 587-592. DOI
  13. Demir, G. N., Uyar, A. S., Ögüdücü, S. G., , In: 9th Annual Conference on Genetic and Evolutionary Computation (GECCO'07), London 2007, pp. 1943-1950. DOI
  14. Devi, M. K., Venkatesh, P., , Future Generation Computer Systems 29 (2013), 262-270. DOI
  15. Eirinaki, M., Gao, J., Varlamis, I., Tserpes, K., , Future Generation Computer Systems 78 (2018), 413-418. DOI
  16. Göksedef, M., Gündüz-Öğüdücü, Ş., , Expert Systems Appl. 37 (2010), 2911-2922. DOI
  17. Golbeck, J., , ACM Trans. Web 3 (2009), 12:1-12:33. DOI
  18. Hasanzadeh, S., Fakhrahmad, S. M., Taheri, M., , The Computer J. 65 (2022), 2, 345-354. DOI
  19. Hofmann, T., , In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99), pp. 50-57. DOI
  20. Kaur, H., Kumar, N., Batra, S., , Future Generation Computer Systems 86 (2018), 297-307. DOI
  21. Kilani, Y., Otoom, A. F., Alsarhan, A., Almaayah, M., , J. Comput. Sci. 28 (2018), 78-93. DOI
  22. Koren, Y., Bell, R., , In: Recommender Systems Handbook 2011, pp. 77-118. DOI
  23. Koren, Y., R, Bell, Volinsky, C., , Computer 42 (2009), 30-37. DOI
  24. Leskovec, J., , In: 8th ACM International Conference on Web Search and Data Mining 2015, pp. 3-4. DOI
  25. Li, Q., Kim, B. M., , Advanced Web Technol. Appl. Lect. Notes Computer Sci. 3007 (2004), 100-110. DOI
  26. Liu, F., Lee, H. J., , Expert Systems Appl. 37 (2010), 4772-4778. DOI
  27. Liu, J., Wu, C., Liu, W., , Decision Support Systems 55 (2013), 838-850. DOI
  28. Najafi, S., Salam, Z., Evaluating prediction accuracy for collaborative filtering algorithms in recommender systems. 
  29. Nilashi, M., Ibrahim, O., Bagherifard, K., , Expert Systems Appl. 92 (2018), 507-520. DOI
  30. Qian, Y., Zhang, Y., Ma, X., Yu, H., Peng, L., , Inform. Fusion 46 (2019), 141-146. DOI
  31. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., , In: 1994 ACM Conference on Computer Supported Cooperative Work (CSCW'94), pp. 175-186. DOI
  32. Resnick, P., Varian, H. R., , Commun. ACM 40 (1997), 56-58. DOI
  33. Sarwar, B., Karypis, G., Konstan, J., Riedl, J., , In: 10th International Conference on World Wide Web (WWW'01), Hong Kong 2001, pp. 285-295. DOI
  34. Sert, S. Y., Ar, Y., Bostancı, G. E., , Turkish J. Electr. Engrg. Comput. Sci. 27 (2019), 3, 2121-2136. DOI
  35. Singh, P. K., Sinha, S., Choudhury, P., , Knowledge Inform. Systems 64 (2022), 665-701. DOI
  36. Tarus, J. K., Niu, Z., Yousif, A., , Future Generation Computer Systems 72 (2017), 37-48. DOI
  37. Tu, Z., Li, W., , Kybernetika 57 (2021), 60-77. MR4231857DOI
  38. Victor, P., Cornelis, C., Cock, M. D., Silva, P. P. da, , Fuzzy Sets Systems 160 (2009), 1367-1382. MR2667643DOI
  39. Yu, W., S.Li, , Future Generation Computer Systems 87 (2018), 312-327. DOI
  40. Zhang, Q., Lu, J., Jin, Y., , Complex Intell. Systems 7 (2021), 1, 439-457. DOI
  41. Zhu, J., He, Y., Zhao, G., Bo, X., Qian, X., , IEEE Trans. Knowledge Data Engrg. (2022). DOI

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