Multiobjective Recommendation for Sustainable Production Systems

We present a recommendation system to help rebuild sustainable production systems. Our multi-objective system synergizes the public and private actors of a territory. From know-how proximities in the Product Space, we suggest productive jumps for companies in a territory that consider the expectations of companies not only in terms of diversification but also in terms of the expectations of local authorities who are anxious to build sustainable production systems. We formalize a multi-stakeholder recommendation that is applied to the sustainability of a territorial economy and we propose the following new objectives to consider:

[1]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[2]  Jianqiang Li,et al.  Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation , 2018, Complex..

[3]  B. Klinger,et al.  Structural Transformation and Patterns of Comparative Advantage in the Product Space , 2006 .

[4]  Robert E. ULANOWlCZ,et al.  Symmetrical overhead in flow networks , 1990 .

[5]  Robert E. Ulanowicz,et al.  Quantifying economic sustainability: Implications for free-enterprise theory, policy and practice , 2009 .

[6]  Bela Balassa,et al.  Comparative advantage in manufactured goods : a re-appraisal , 1986 .

[7]  J. Sauvage.  The Stringency of Environmental Regulations and Trade in Environmental Goods , 2014 .

[8]  Maoguo Gong,et al.  Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier] , 2015, IEEE Computational Intelligence Magazine.

[9]  Diana Genkova.  Modeling of the Human Needs: an Economic Interpretation of Maslow’s Theory of Motivation , 2021 .

[10]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[11]  A. Albouy-Kissi,et al.  Production2Vec: a hybrid recommender system combining semantic and product complexity approach to improve industrial resiliency , 2021, ICAIIS.

[12]  Bamshad Mobasher,et al.  Controlling Popularity Bias in Learning-to-Rank Recommendation , 2017, RecSys.

[13]  Dietmar Jannach,et al.  Multistakeholder recommendation: Survey and research directions , 2020, User Modeling and User-Adapted Interaction.

[14]  Claire Mathieu,et al.  Maximizing profit using recommender systems , 2009, ArXiv.

[15]  Henrique Pacini,et al.  Mapping Green Product Spaces of Nations , 2013 .

[16]  A. Maslow.  A Theory of Human Motivation , 1943 .

[17]  Carlos Llano Verduras,et al.  Measuring subnational economic complexity: An application with Spanish data , 2019 .

[18]  Bert Huang,et al.  New Fairness Metrics for Recommendation that Embrace Differences , 2017, ArXiv.

[19]  Jesus Felipe,et al.  Product Complexity and Economic Development , 2010 .

[20]  Lise Getoor,et al.  A Fairness-aware Hybrid Recommender System , 2018, ArXiv.

[21]  Bamshad Mobasher,et al.  Recommender Systems as Multistakeholder Environments , 2017, UMAP.

[22]  P. Mealy,et al.  Economic Complexity and the Green Economy , 2017, Research Policy.

[23]  W. H. M. Zijm,et al.  Exploring recommendations for circular supply chain management through interactive visualisation , 2021, Decis. Support Syst..

[24]  Hoda Ghavamipoor,et al.  Designing a model of decision making in layers of supply, manufacturing, and distribution of the supply chain: A recommender-based system , 2014, 8th International Conference on e-Commerce in Developing Countries: With Focus on e-Trust.

[25]  Weiwen Liu,et al.  Personalizing Fairness-aware Re-ranking , 2018, ArXiv.

[26]  Robert E. Ulanowicz,et al.  Quantifying sustainability: Resilience, efficiency and the return of information theory , 2009 .

[27]  Robert E. Ulanowicz,et al.  Some steps toward a central theory of ecosystem dynamics , 2003, Comput. Biol. Chem..

[28]  Putra Manggala,et al.  Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations , 2018, UMAP.

[29]  A. Kharrazi,et al.  Advancing Empirical Approaches to the Concept of Resilience: A Critical Examination of Panarchy, Ecological Information, and Statistical Evidence , 2016 .

[30]  Laizhong Cui,et al.  A novel multi-objective evolutionary algorithm for recommendation systems , 2017, J. Parallel Distributed Comput..

[31]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[32]  César A. Hidalgo,et al.  The building blocks of economic complexity , 2009, Proceedings of the National Academy of Sciences.

[33]  姜哲,et al.  韧性（Resilience）的概念分析 , 2008 .

[34]  Fernando Diaz,et al.  Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems , 2018, CIKM.

[35]  Robin D. Burke,et al.  Multisided Fairness for Recommendation , 2017, ArXiv.

[36]  Barry Smyth,et al.  Similarity vs. Diversity , 2001, ICCBR.

[37]  Manju R. Agrawal,et al.  A sub-national economic complexity analysis of Australia’s states and territories , 2018 .

[38]  César A. Hidalgo,et al.  The Product Space Conditions the Development of Nations , 2007, Science.

[39]  Manuel Otero,et al.  Methods of Analysis for a Sustainable Production System , 2011 .

[40]  Bamshad Mobasher,et al.  Towards Multi-Stakeholder Utility Evaluation of Recommender Systems , 2016, UMAP.

[41]  Robin Burke,et al.  Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness , 2019, RMSE@RecSys.

[42]  Vito Albino,et al.  Green product development: What does the country product space imply? , 2018 .

[43]  Nikola Milojković,et al.  Multi-Gradient Descent for Multi-Objective Recommender Systems , 2020, ArXiv.

[44]  Richard Schmalensee,et al.  The New Economics of Multi-Sided Platforms: A Guide to the Vocabulary , 2016 .

[45]  Karoline S. Rogge,et al.  Specialisation , Diversification and the Ladder of Green Technology Development , 2022 .

[46]  Andrés Gómez-Liévano,et al.  Methods and Concepts in Economic Complexity , 2018, 1809.10781.

[47]  Maoguo Gong,et al.  Multi-objective optimization for long tail recommendation , 2016, Knowl. Based Syst..

[48]  Georg Zachmann,et al.  Green exports and the global product space: Prospects for EU industrial policy. Bruegel Working Paper 2011/07, May 2011 , 2011 .

[49]  César A. Hidalgo,et al.  The Atlas of Economic Complexity: Mapping Paths to Prosperity , 2011 .

[50]  J. Rochet,et al.  Platform competition in two sided markets , 2003 .

[51]  Alexander Teytelboym,et al.  Science Journals — AAAS , 2017 .

[52]  Anísio Lacerda,et al.  Multiobjective Pareto-Efficient Approaches for Recommender Systems , 2014, ACM Trans. Intell. Syst. Technol..

[53]  Elena Rovenskaya,et al.  Network structure impacts global commodity trade growth and resilience , 2017, PloS one.

[54]  Bruno Agard,et al.  Recommender systems as an agility enabler in supply chain management , 2020, Journal of Intelligent Manufacturing.