Abstract: We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching, where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers’ knowledge demands with a quick response and quality service in the knowledge push system. To improve the previous work, two methods are investigated to augment the limited training set in practical operations, namely, oscillating the feature weight and revising the case feature in the case feature vectors. In addition, we propose a multi-classification radial basis function neural network that can match the knowledge from the knowledge base once and ensure the accuracy of pushing results. We apply our approach using the training set in the design of guides by computer numerical control machine tools for training and testing, and the results demonstrate the benefit of the augmented training set. Moreover, experimental results reveal that our approach outperforms other matching approaches.

知识推送系统中一种基于多分类径向基神经网络的知识匹配方法

[1]Bahramian Z, Abbaspour RA, Claramunt C, 2017. A cold start context-aware recommender system for tour planning using artificial neural network and case based reasoning. Mob Inform Syst, 2017:9364903.

[2]Chen S, Yang ZY, Sun LY, et al., 2015. Research on design knowledge analytical method during sketching—combining acoustic energy feature and creative segment theory. J Zhejiang Univ Eng Sci, 49(11):2073-2082 (in Chinese).

[3]Devi MKK, Samy RT, Kumar SV, et al., 2010. Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems. Proc IEEE Int Conf on Computational Intelligence and Computing Research, p.1-4.

[4]Dong SY, Xu JX, Wang KQ, et al., 2013. Active push model of manufacturing process knowledge in CAD platform based on immune process. Comput Integr Manuf Syst, 19(7):1520-1531 (in Chinese).

[5]Fan ZP, Feng Y, Sun YH, et al., 2005. A framework on compound knowledge push system oriented to organizational employees. Proc 1^st Int Workshop on Internet and Network Economics, p.622-630.

[6]Feng YX, Zhang SY, Gao YC, et al., 2016. Intelligent push method of CNC design knowledge based on feature semantic analysis. Comput Integr Manuf Syst, 22(1):189-201 (in Chinese).

[7]Gabrani G, Sabharwal S, Singh VK, 2017. Artificial intelligence based recommender systems: a survey. Proc 1^st Int Conf on Advances in Computing and Data Sciences, p.50-59.

[8]Guo Y, Yin CX, Li MF, et al., 2018. Mobile e-commerce recommendation system based on multi-source information fusion for sustainable e-business. Sustainability, 10(1):147.

[9]Gupta A, Tripathy BK, 2014. A generic hybrid recommender system based on neural networks. Proc IEEE Int Advance Computing Conf, p.1248-1252.

[10]Ji X, Gu XJ, Dai F, et al., 2013. Technology for product design knowledge push based on ontology and rough sets. Comput Integr Manuf Syst, 19(1):7-20 (in Chinese).

[11]Jiang H, Yin P, Guo L, et al., 2017. Knowledge push based on design flow and user capacity model. Proc MATEC Web Conf, Article 12.

[12]Karayiannis NB, 1999. Reformulated radial basis neural networks trained by gradient descent. IEEE Trans Neur Netw, 10(3):657-671.

[13]Le CY, Dai F, Ji X, et al., 2010. Domain knowledge actively pushing system driven by process. Comput Integr Manuf Syst, 16(12):2720-2727 (in Chinese).

[14]Li XR, Yu SH, Chu JJ, et al., 2017. Double push strategy of knowledge for product design based on complex network theory. Discr Dynam Nat Soc, Article 2 078 626.

[15]Liang Y, Zhang S, Liu X, et al., 2015. Product design knowledge dynamic push technology based on variable-weight layered spreading activation model. Comput Integr Manuf Syst, 21(12):3107-3118 (in Chinese).

[16]Liu HM, Wang HQ, Li X, 2009. A study on data normalization for target recognition based on RPROP algorithm. Mod Radar, 31(5):55-60.

[17]Liu TY, Wang HF, He Y, 2016. Intelligent knowledge recommending approach for new product development based on workflow context matching. Concurr Eng, 24(4):318-329.

[18]Paradarami TK, Bastian ND, Wightman JL, 2017. A hybrid recommender system using artificial neural networks. Expert Syst Appl, 83:300-313.

[19]Pushpa CN, Ashvini P, Thriveni J, et al., 2013. Web page recommendations using radial basis neural network technique. Proc 8^th Int Conf on Industrial and Information Systems, p.501-506.

[20]Schreiber AT, Schreiber G, Akkermans H, et al., 2000. Knowledge engineering and management: the common KADS methodology. MIT Press, Cambridge, MA, USA.

[21]Sunhem W, Pasupa K, 2016. An approach to face shape classification for hairstyle recommendation. Proc 8^th Int Conf on Advanced Computational Intelligence, p.390-394.

[22]Twardowski B, 2016. Modelling contextual information in session-aware recommender systems with neural networks. Proc 10^th ACM Conf on Recommender Systems, p.273-276.

[23]van Rijsbergen CJ, 1979. Information Retrieval (2^nd Ed.). Butterworth, London, UK.

[24]Wang ZS, Tian L, Wu YH, et al., 2016. Personalized knowledge push system based on design intent and user interest. Proc Inst Mech Eng Part C, 230(11):1757-1772.

[25]Wu H, Zhang ZX, Yue K, et al., 2018. Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl-Based Syst, 145:46-58.

[26]Wu LJ, Gou BC, Wen CG, 2018. Research on knowledge-push driven by workflow and knowledge points. Comput Eng Appl, 54(4):231-236 (in Chinese).

[27]Xiao Y, Lou CQ, Liu G, 2010. Personalized knowledge push service based on semantic web. Int Conf on E-Business and E-Government, p.1872-1875.

[28]Xu YH, Yin GF, Nie Y, et al., 2013. Research on an active knowledge push service based on collaborative intent capture. J Netw Comput Appl, 36(6):1418-1430.

[29]Xue HJ, Dai XY, Zhang JB, et al., 2017. Deep matrix factorization models for recommender systems. Proc 26^th Int Joint Conf on Artificial Intelligence, p.3203-3209.

[30]Yan Y, Yang N, Hao J, et al., 2016. A context modeling method of knowledge recommendation for designers. Proc Int Conf on Information System and Artificial Intelligence, p.492-496.

[31]Yang XH, Huang JF, Wang JW, et al., 2007. Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network. J Zhejiang Univ-Sci A (Appl Phys Eng), 8(6):883-859.

[32]Zhang C, Zhou GH, Bai QD, et al., 2018. HEKM: a high-end equipment knowledge management system for supporting knowledge-driven decision-making in new product development. Proc ASME Int Design Engineering Technical Conf and Computers and Information in Engineering Conf, Article V0 1BT 02A 014.

[33]Zhang FP, Li L, 2016. Research on knowledge push method for business process based on multidimensional hierarchical context model. Proc IEEE Int Conf on Industrial Engineering and Engineering Management, p.656-660.

[34]Zhang FP, Li L, 2017. Research on knowledge push method for business process based on multidimensional hierarchical context model. J Comput-Aided Des Comput Graph, 29(4):751-758 (in Chinese).

[35]Zhang K, Zhao W, Wang J, et al., 2019. Knowledge push technology based on quality function knowledge deployment. Proc Inst Mech Eng Part C, 233(4):1119-1138.

[36]Zhang LL, Nie GL, Zhang YJ, et al., 2009. A way to implement intelligent knowledge push in knowledge management system. Proc Int Joint Conf on Computational Sciences and Optimization, 1:746-749.

[37]Zhang SY, Gu Y, Liu X, et al., 2018. A knowledge push technology based on applicable probability matching and multidimensional context driving. Front Inform Technol Electron Eng, 19(2):235-245.

[38]Zhang SY, Gu Y, Yi GD, 2019. A hybrid knowledge push method based on trust-aware and item-cluster oriented to product design. New Gener Comput, 37:339-357.

[39]Zuo Y, Zeng J, Gong M, et al., 2016. Tag-aware recommender systems based on deep neural networks. Neurocomputing, 204:51-60.