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## Zinc ore supplier evaluation and recommendation method based on nonlinear adaptive online transfer learning

 932 Lushan South Road, Yuelu District, Changsha, Hunan Province, China

* Corresponding author: Yonggang Li

Received  March 2021 Revised  August 2021 Early access November 2021

Fund Project: The first author is supported by NNSFC grant 61973321

Purchasing decisions determine the purchasing cost, which is the largest section of the production cost of zinc smelting enterprise(ZSE). An excellent supplier recommendation is significant for ZSE to reduce the cost. However, during the supplier recommendation process, the nonlinear demand feature of purchasing department varies with the production environment, and there are wrong samples that can affect the supplier recommendation effect. To handle these problems, the recommendation strategy based on a multiple-layer perceptron adaptive online transfer learning algorithm(AOTLMLP) are proposed. In this method, the original prediction function is modified based on MLP nonlinear projective function and adaptive loss function, which enables the AOTLMLP algorithm to tackle the nonlinear classification problems and efficiently follow the demand change of purchasing department, thereby improving the result of the recommendation. The performance of the AOTLMO algorithm is evaluated through a common dataset and a purchasing dataset from a zinc smelter that generated by a supplier evaluation model. It can be assumed that AOTLMLP can ignore the influence of wrong samples and provide an effective recommendation confronting the characteristic of zinc ore purchasing.

Citation: Yudong Li, Yonggang Li, Bei Sun, Yu Chen. Zinc ore supplier evaluation and recommendation method based on nonlinear adaptive online transfer learning. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021193
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show all references

##### References:
 [1] G. Akman, Evaluating suppliers to include green supplier development programs via fuzzy c-means and vikor methods, Computers & Industrial Engineering, 86 (2015), 69-82.  doi: 10.1016/j.cie.2014.10.013.  Google Scholar [2] M. Balabanović and Y. Shoham, Fab: Content-based, collaborative recommendation, Communications of the ACM, 40 (1997), 66-72.  doi: 10.1145/245108.245124.  Google Scholar [3] Z. Chen, Y. Jiang and Y. Zhao, A collaborative filtering recommendation algorithm based on user interest change and trust evaluation, International Journal of Digital Content Technology and its Applications, 4 (2010), 106-113.   Google Scholar [4] J. Cheng, Y. Liu, H. Zhang, X. Wu and F. Chen, A new recommendation algorithm based on user's dynamic information in complex social network, Mathematical Problems in Engineering, 2015 (2015), Article ID 281629. doi: 10.1155/2015/281629.  Google Scholar [5] G. Ditzler, M. Roveri, C. Alippi and R. Polikar, Learning in nonstationary environments: A survey, IEEE Computational Intelligence Magazine, 10 (2015), 12-25.  doi: 10.1109/MCI.2015.2471196.  Google Scholar [6] M. M. Gaber, A. Zaslavsky and S. Krishnaswamy, Mining data streams: A review, ACM Sigmod Record, 34 (2005), 18-26.  doi: 10.1145/1083784.1083789.  Google Scholar [7] J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy and A. Bouchachia, A survey on concept drift adaptation, ACM Computing Surveys (CSUR), 46 (2014), 1-37.  doi: 10.1145/2523813.  Google Scholar [8] I. Gasmi, H. Seridi-Bouchelaghem, L. Hocine and B. Abdelkarim, Collaborative filtering recommendation based on dynamic changes of user interest, Intelligent Decision Technologies, 9 (2015), 271-281.  doi: 10.3233/IDT-140221.  Google Scholar [9] T. Grubinger, G. C. Chasparis and T. 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Zhang, A library recommender system using interest change over time and matrix clustering, in International Conference on Asian Digital Libraries, Springer, 2012,259–268. doi: 10.1007/978-3-642-34752-8_32.  Google Scholar [15] K. Lang, Newsweeder: Learning to filter netnews, in Machine Learning Proceedings 1995, 1995, 331–339. doi: 10.1016/B978-1-55860-377-6.50048-7.  Google Scholar [16] J. Li, W. Qiu and W. Li, An improved k-means algorithm for supplier evaluation and recommendation of purchase and supply platform, in Journal of Physics: Conference Series, vol. 1650 (2020), 032165. doi: 10.1088/1742-6596/1650/3/032165.  Google Scholar [17] Y.-H. Lin and L. Chang, An online transfer learning framework for time-varying distribution data prediction., Google Scholar [18] S. Luthra, K. Govindan, D. Kannan, S. K. Mangla and C. P. Garg, An integrated framework for sustainable supplier selection and evaluation in supply chains, Journal of Cleaner Production, 140 (2017), 1686-1698.  doi: 10.1016/j.jclepro.2016.09.078.  Google Scholar [19] C. Lv, Y. Lu, X. Yan, W. Lu and H. Tan, Supplier recommendation based on knowledge graph embedding, in 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID), IEEE, 2020,514–518. doi: 10.1109/MSIEID52046.2020.00105.  Google Scholar [20] A. Niyazov, E. Mikhailova and O. Egorova, Content-based music recommendation system, in 2021 29th Conference of Open Innovations Association (FRUCT), IEEE, 2021,274–279. doi: 10.23919/FRUCT52173.2021.9435533.  Google Scholar [21] R. W. Saaty, The analytic hierarchy process-what it is and how it is used, Mathematical Modelling, 9 (1987), 161-176.  doi: 10.1016/0270-0255(87)90473-8.  Google Scholar [22] U. Thakker, R. Patel and M. Shah, A comprehensive analysis on movie recommendation system employing collaborative filtering, Multimedia Tools and Applications, 1–26. Google Scholar [23] Q. Wu, X. Zhou, Y. Yan, H. Wu and H. Min, Online transfer learning by leveraging multiple source domains, Knowledge and Information Systems, 52 (2017), 687-707.  doi: 10.1007/s10115-016-1021-1.  Google Scholar [24] P. Zhao, S. C. H. Hoi, J. Wang and B. Li, Online transfer learning, Artificial Intelligence, 216 (2014), 76-102.  doi: 10.1016/j.artint.2014.06.003.  Google Scholar [25] I. Žliobaitė, Learning under concept drift: An overview, arXiv preprint, arXiv: 1010.4784. Google Scholar
The framework of evaluation criterias for zinc ore suppliers
The requirement change problem in supplier recommendation
The structure of nonlinear prediction function based on MLP
The update steps of algorithm
Cumulative trainning error rate for different $\beta$, considering IMAGE dataset
Cumulative trainning error rate for different $\beta$, considering Purchasing dataset
Cumulative trainning error rate for different layer parameter, considering IMAGE dataset
Cumulative trainning error rate for different layer parameter, considering Purchasing dataset
Cumulative trainning error rate for different $\eta$, considering training purchasing dataset
Cumulative trainning error rate for different $\eta$, considering testing purchasing dataset
Recommendation accuracy for different $\eta$, for purchasing demand change
Cumulative trainning error rate for different $\varphi$, considering training purchasing dataset
Cumulative trainning error rate for different $\varphi$, considering testing purchasing dataset
Recommendation accuracy for different $\varphi$, for purchasing demand change
The convergence behaviors for different learning strategy, considering purchasing dataset
The performance of four algorithms, considering purchasing demand change
Symbol reference table
 Symbol Paraphrase ${{\bf{x}}_t}$ Supplier feature vector ${y_t}$ Recommendation outcome ${\bf{v}}$ Linear previous demand feature vector ${{\bf{w}}_t}$ Linear present demand feature vector(time-varying) ${{\bf{v}}_\phi }$ Nonlinear previous demand feature matrix ${{\bf{w}}_{\phi t}}$ Nonlinear present demand feature matrix(time-varying) ${{\bf{z}}_t}$ The hidden layer node vector(time-varying) ${{\bf{z}}_{(j)t}}$ The jth hidden layer node vector(time-varying) ${{\bf{r}}_t}$ The weight vector for ReLU units(time-varying) ${{\bf{r}}_{(j)t}}$ The jth layer weight vector for ReLU units(time-varying) $\beta$ The restriction parameter $\varphi$ The preference parameter $\eta$ The transfer speed rate
 Symbol Paraphrase ${{\bf{x}}_t}$ Supplier feature vector ${y_t}$ Recommendation outcome ${\bf{v}}$ Linear previous demand feature vector ${{\bf{w}}_t}$ Linear present demand feature vector(time-varying) ${{\bf{v}}_\phi }$ Nonlinear previous demand feature matrix ${{\bf{w}}_{\phi t}}$ Nonlinear present demand feature matrix(time-varying) ${{\bf{z}}_t}$ The hidden layer node vector(time-varying) ${{\bf{z}}_{(j)t}}$ The jth hidden layer node vector(time-varying) ${{\bf{r}}_t}$ The weight vector for ReLU units(time-varying) ${{\bf{r}}_{(j)t}}$ The jth layer weight vector for ReLU units(time-varying) $\beta$ The restriction parameter $\varphi$ The preference parameter $\eta$ The transfer speed rate
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