Configuring products with natural language: a simple yet effective approach based on text embeddings and multilayer perceptron

Product configurators are recognised as critical toolkits enabling customers to co-create products with companies. Most available product configurators require customers to select suitable product ...

[1]  Linda L. Zhang,et al.  Product configuration: a review of the state-of-the-art and future research , 2014 .

[2]  Marieke van Erp,et al.  Lessons learnt from the Named Entity rEcognition and Linking (NEEL) challenge series , 2017, Semantic Web.

[3]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[4]  Shu-Hsuan Chang,et al.  Applying case-based reasoning for product configuration in mass customization environments , 2005, Expert Syst. Appl..

[5]  Andrew Y. C. Nee,et al.  Web-based configuration design system for product customization , 2006 .

[6]  Lars Hvam,et al.  The main challenges for manufacturing companies in implementing and utilizing configurators , 2018, Comput. Ind..

[7]  H. Xie *,et al.  Modelling and solving engineering product configuration problems by constraint satisfaction , 2005 .

[8]  C.-C. Chen,et al.  Coordinating product configuration in the order fulfilment processing: an approach based on the binary tree algorithm , 2006, Int. J. Comput. Integr. Manuf..

[9]  Qi Wang,et al.  Optimisation of product configuration in consideration of customer satisfaction and low carbon , 2017, Int. J. Prod. Res..

[10]  Lars Hvam,et al.  A layout technique for class diagrams to be used in product configuration projects , 2010, Comput. Ind..

[11]  Alexander Felfernig,et al.  Towards recommending configurable offerings , 2010 .

[12]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[13]  Yue Wang,et al.  Needs-Based Product Configurator Design for Mass Customization Using Hierarchical Attention Network , 2021, IEEE Transactions on Automation Science and Engineering.

[14]  John R. Hauser,et al.  Identifying Customer Needs from User-Generated Content , 2019, Mark. Sci..

[15]  Chuntao Liu,et al.  Customer-driven product configuration optimization for assemble-to-order manufacturing enterprises , 2008 .

[16]  Lars Hvam,et al.  Guidelines for Structuring Object-Oriented Product Configuration Models in Standard Configuration Software , 2020, J. Univers. Comput. Sci..

[17]  Yue Wang,et al.  An approach to improve the efficiency of configurators , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[18]  Mitchell M. Tseng,et al.  Mapping customer needs to design parameters in the front end of product design by applying deep learning , 2018 .

[19]  Michel Aldanondo,et al.  Optimisation of the concurrent product and process configuration: an approach to reduce computation time with an experimental evaluation , 2019, Int. J. Prod. Res..

[20]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[21]  Karl T. Ulrich,et al.  Research Note: User Design of Customized Products , 2007 .

[22]  Tai-Hsi Wu,et al.  Solutions for product configuration management: An empirical study , 2005, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[23]  Chao Liu,et al.  Personalized product configuration framework in an adaptable open architecture product platform , 2017 .

[24]  F. Piller,et al.  Cracking the Code of Mass Customization , 2009 .

[25]  Yue Wang,et al.  Configuration-Based Smart Customization Service: A Multitask Learning Approach , 2020, IEEE Transactions on Automation Science and Engineering.

[26]  David W. Franke Configuration research and commercial solutions , 1998, Artif. Intell. Eng. Des. Anal. Manuf..

[27]  C. Forza,et al.  Product configurator impact on product quality , 2012 .

[28]  Arnaud De Bruyn,et al.  Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids , 2008, Mark. Sci..

[29]  Cipriano Forza,et al.  Sales configurator capabilities to avoid the product variety paradox: Construct development and validation , 2013, Comput. Ind..

[30]  Dong Yang,et al.  Applying constraint satisfaction approach to solve product configuration problems with cardinality-based configuration rules , 2013, J. Intell. Manuf..

[31]  Guoyin Wang,et al.  Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms , 2018, ACL.

[32]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Yoshua Bengio,et al.  Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.

[34]  John P. McDermott,et al.  R1: A Rule-Based Configurer of Computer Systems , 1982, Artif. Intell..

[35]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[36]  Andrew Kusiak,et al.  Optimising product configurations with a data-mining approach , 2009 .

[37]  Lukumon O. Oyedele,et al.  Deep learning in the construction industry: A review of present status and future innovations , 2020 .

[38]  Yu Yang,et al.  A product configuration approach based on online data , 2019, J. Intell. Manuf..

[39]  Mitchell M. Tseng,et al.  Mass customisation as a collaborative engineering effort , 2009 .

[40]  Tim Menzies,et al.  Easy over hard: a case study on deep learning , 2017, ESEC/SIGSOFT FSE.