Abstract
Recommendation systems are successful personalizing tools and information filtering in web. One of the most important recommendation methods is matrix factorization method. In matrix factorization method, the latent features of users and items are determined in such a way that the inner product of the latent features of a user with the latent features of an item is equal to that user's rating on that item. This model is solved using alternate optimization algorithm. The solution and the prediction error of this algorithm depend on the initial values of the latent features of users which are usually set to small random values. The purpose of this paper is to propose a fast alternate optimization algorithm for matrix factorization which converges to a good solution. To do so, firstly, we show experimentally that if the latent feature vector of each user is initialized by a vector of which elements are equal, we can also obtain a proper solution using the alternate optimization algorithm. Then, we prove that if our proposed initialization method is used, the alternate optimization algorithm for matrix factorization can be simplified using Sherman–Morrison formula. Experimental results on 5 real datasets show that the runtime of our proposed algorithm is 2–45 times less than the traditional method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bertsekas D (2016) Nonlinear programming, (optimization and computation), 3rd edn. Athena Scientific, New York
Bobadilla J, Alonso S, Hernando AJAS (2020) Deep learning architecture for collaborative filtering recommender systems. Appl Sci 10(7):2441
Bu J, Shen X, Xu B, Chen C, He X, Cai D (2016) Improving collaborative recommendation via user-item subgroups. IEEE Trans Knowl Data Eng 28(9):2363–2375
Cai W et al (2019) Neighborhood-enhanced transfer learning for one-class collaborative filtering. Neurocomputing 341:80–87
Chae D-K, Lee S-C, Lee S-Y, Kim S-W (2018) On identifying k-nearest neighbors in neighborhood models for efficient and effective collaborative filtering. Neurocomputing 278:134–143
Chen J, Wang C, Shi Q, Feng Y, Chen C (2019) Social recommendation based on users’ attention and preference. Neurocomputing 341:1–9
Condli MK, Lewis DD, Madigan D, Posse C (1999) Bayesian mixed-e ects models for recommender systems. In: ACM SIGIR. p 99
Deepa N, Pandiaraja PJSC (2019) Hybrid context aware recommendation system for E-health care by Merkle Hash tree from cloud using evolutionary algorithm. Soft Comput 24:1–13
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1–30
Gholami A, Forghani Y, Branch M (2020) Improving multi-class co-clustering-based collaborative recommendation using item tags improving multi-class co-clustering-based collaborative recommendation using item tags. Rev d’Intell Artif 34:59–65
Goodfellow I, Sulthana S, Ming M (2014) Generative adversarial nets. In: Advances in neural information processing systems. pp 2672–2680
Hasanzadeh N, Forghani Y (2019) Improving the accuracy of M-distance based nearest neighbor recommendation system by using ratings variance. Ingénierie Syst Inf 24(2):131–137
Jain G, Mahara T, Tripathi KN (2020) A survey of similarity measures for collaborative filtering-based recommender system. In: Suzuki Y, Ovaska SJ, Furuhashi T, Roy R, Dote Y (eds) Soft computing: theories and applications. Springer, Berlin, pp 343–352
Khadem MM, Forghani Y (2020) A recursive algorithm to increase the speed of regression-based binary recommendation systems. Inf Sci 512:1324–1334
Kumar V, Pujari AK, Sahu SK, Kagita VR, Padmanabhan V (2017a) Collaborative filtering using multiple binary maximum margin matrix factorizations. Inf Sci 380:1–11
Kumar V, Pujari AK, Sahu SK, Kagita VR, Padmanabhan V (2017b) Proximal maximum margin matrix factorization for collaborative filtering. Pattern Recognit Lett 86:62–67
Lara-Cabrera R, González-Prieto Á, Ortega FJAS (2020) Deep matrix factorization approach for collaborative filtering recommender systems. Appl Sci 10(14):4926
Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM international conference on data mining, Newport Beach. SIAM, pp 471–475
Li J, Sun L, Wang J (2011) A slope one collaborative filtering recommendation algorithm using uncertain neighbors optimizing. In: International conference on web-age information management. Springer, Berlin, pp 160–166
Liu D, Ye XJK-BS (2020) A matrix factorization based dynamic granularity recommendation with three-way decisions. Knowl Based Syst 191:105243
Liu J, Pan W, Ming Z (2020) CoFiGAN: Collaborative filtering by generative and discriminative training for one-class recommendation. Knowl Based Syst 191:105255
Pan Y, He F, Yu H (2019) A novel enhanced collaborative autoencoder with knowledge distillation for top-N recommender systems. Neurocomputing 332:137–148
Park Y, Park S, Jung W, Lee S-G (2015) Reversed CF: a fast collaborative filtering algorithm using a k-nearest neighbor graph. Expert Syst Appl 42(8):4022–4028
Qi J, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl Based Syst 91(1):143–151
Ren L, Wang W (2018) An SVM-based collaborative filtering approach for Top-N web services recommendation. Future Gener Comput Syst 78(1):531–543
Salter J, Antonopoulos N (2006) CinemaScreen recommender agent: combining collaborative and content-based filtering. IEEE Intell Syst 21(1):35–41
Sulthana AR, Gupta M, Subramanian S, Mirza SJSC (2020) Improvising the performance of image-based recommendation system using convolution neural networks and deep learning. Soft Comput 24:1–14
Van Meteren R, Van Someren M (2000) Using content-based filtering for recommendation. In: Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop, Barcelona. pp 47–56
Vapnik V (1995) The nature of statisticsal learning. Springer, New York
Wang Q-X, Luo X, Li Y, Shi X-Y, Gu L, Shang M-SJN (2018) Incremental slope-one recommenders. Neurocomputing 272(1):606–618
Wang D et al (2019) Elastic-net regularized latent factor analysis-based models for recommender systems. Neurocomputing 329:66–74
Xing S, Liu FA, Wang Q, Zhao X, Li T (2019) A hierarchical attention model for rating prediction by leveraging user and product reviews. Neurocomputing 332:417–427
Yadalam TV, Gowda VM, Kumar VS, Girish D, Namratha M (2020) Career recommendation systems using content based filtering. In: 2020 5th international conference on communication and electronics systems (ICCES). IEEE, pp 660–665
Yao Y (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180(3):341–353
Yao Y (2015) Rough sets and three-way decisions. In: International conference on rough sets and knowledge technology, Tianjin. Springer, pp 62–73
Yuan Y, Luo X, Shang M-S (2018) Effects of preprocessing and training biases in latent factor models for recommender systems. Neurocomputing 275(1):2019–2030
Zheng M, Min F, Zhang H-R, Chen W-B (2016) Fast recommendations with the m-distance. IEEE Access 4(1):1464–1468
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All the authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mohammadian, M., Forghani, Y. & Torshiz, M.N. An initialization method to improve the training time of matrix factorization algorithm for fast recommendation. Soft Comput 25, 3975–3987 (2021). https://doi.org/10.1007/s00500-020-05419-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05419-0