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Keynote: Quantum strategies, simulation, big data, and hybrid intelligent systems for business, management & engineering

Shuliang Li

Published Jan 1, 2017• 0 citations
OpenAlex

Abstract

Quantum strategies, hybrid density analysis with interaction equilibrium dynamics and quantum evolution, quantum knowledge management, big data, and hybrid intelligent systems for business, management & engineering.

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Recommender systems are an Artificial Intelligence technology that has become an essential part of business for many industries and businesses.Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user.The system learns from a customer and recommends products that she will find most valuable from among the available products.They serve many types of E-commerce applications, from direct product recommendation for an individual to helping someone find a gift for a third party [10].Recently the world of the web has become more social and more real-time.Facebook and Twitter [16] are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research.In this research project, the researcher tries to provide a present an analysis of how recommender systems can be used in E-commerce today, companies, SMEs, and other social institutions to improve sales, maintain good customer relationship and loyalty, and saves customers sourcing time.Recommender Systems too can also be used to analyze Big Data, although there are some key challenges associated with Big Data analytics.The impact of data abundance extends well beyond business [15].But the computer tools for gleaning knowledge and insights from the Internet era's vast trove of unstructured data are fast gaining ground.At the forefront are the rapidly advancing techniques of artificial intelligence like natural-language processing, pattern recognition and machine learning.The researcher hopes that these key problems with improved recommender systems in the future: hybrid data, predictable recommendations, scalability, and incorporation of content, such problems could be resolved.If recommender systems are able to surmount these challenges, they have the potential to become an essential component of doing business in Ecommerce

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This keynote speech covers quantum games, quantum models, intelligent systems and simulation for business and management. In particular, the following research work will be presented. \n-\tA survey of the current status of research on quantum games (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for competition (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for cooperation (Dingxuan Huang & Shuliang Li)\n-\tSocial media for brand virality and open innovation\n-\tIntelligent systems and multi-agent-based systems for strategic decision making (Shuliang Li & Jim Zheng Li)\n-\tModelling & computer simulation of enterprise carbon intangible asset evolution; and a resilient multi-factor elasticity framework for low carbon cities (Yin Zeng & Shuliang Li)
Recommender systems are an Artificial Intelligence technology that has become an essential part of business for many industries and businesses.Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user.The system learns from a customer and recommends products that she will find most valuable from among the available products.They serve many types of E-commerce applications, from direct product recommendation for an individual to helping someone find a gift for a third party [10].Recently the world of the web has become more social and more real-time.Facebook and Twitter [16] are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research.In this research project, the researcher tries to provide a present an analysis of how recommender systems can be used in E-commerce today, companies, SMEs, and other social institutions to improve sales, maintain good customer relationship and loyalty, and saves customers sourcing time.Recommender Systems too can also be used to analyze Big Data, although there are some key challenges associated with Big Data analytics.The impact of data abundance extends well beyond business [15].But the computer tools for gleaning knowledge and insights from the Internet era's vast trove of unstructured data are fast gaining ground.At the forefront are the rapidly advancing techniques of artificial intelligence like natural-language processing, pattern recognition and machine learning.The researcher hopes that these key problems with improved recommender systems in the future: hybrid data, predictable recommendations, scalability, and incorporation of content, such problems could be resolved.If recommender systems are able to surmount these challenges, they have the potential to become an essential component of doing business in Ecommerce