Big Data Driven Recommender System for Enterprise-User Interaction Innovation

LUO Ting-yu; XIE Kang; LIU Yi

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Journal of Beijing Jiaotong University（Social Sciences Edition） ›› 2023, Vol. 22 ›› Issue (1) : 33-45.

Applied Economics Studies

Big Data Driven Recommender System for Enterprise-User Interaction Innovation

LUO Ting-yu¹, XIE Kang¹, LIU Yi²

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Abstract

Big data driven enterprise-user interaction innovation is a basic form of digitized enterprise-user interaction innovation， and digitized enterprise-user interaction innovation is an innovation mode of digital economy. The innovation-oriented recommender system is a tool for digitized enterprise-user interaction innovation. Different from user-centric recommendation systems， this article focuses on recommender systems for enterprise-user interaction innovation， and analyzes the requirements of enterprise-user interactive innovation recommender system from three aspects of data， system characteristics and application evaluation. It proposes a recommender system design framework consisting of four parts： application scenarios， data basis， recommendation system characteristics and system evaluation. This framework analyzes the influence of product innovation tasks on the innovation scenarios， and the influence of product attributes and application modules on the design of recommendation systems in marketing scenarios， so as to offer guidance to the design of Big Data Driven Recommender System for Enterprise-User Interaction Innovation.

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big data / recommender system / enterprise-user interaction innovation / system design

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LUO Ting-yu, XIE Kang, LIU Yi. Big Data Driven Recommender System for Enterprise-User Interaction Innovation[J]. Journal of Beijing Jiaotong University（Social Sciences Edition）. 2023, 22(1): 33-45

References

1 肖静华，吴瑶，刘意，等.消费者数据化参与的研发创新：企业与消费者协同演化视角的双案例研究［J］.管理世界，2018，（8）：154－192.
2 谢康，肖静华，王茜.大数据驱动的企业与用户互动研发创新［J］.北京交通大学学报（社会科学版），2018，17（2）：18－26.
3 JANNACH D， ZANKER M， GE M，et al. Recommender systems in computer science and information systems–a landscape of research［C］// International conference on electronic commerce and web technologies. Berlin：Springer， 2012， 76－87.
4 VENKATRAMAN S， KAMATKAR S J. Intelligent information retrieval and recommender system framework［J］. International Journal of Future Computer and Communication， 2013， 2（2）： 85－89.
5 FERNA?NDEZ-GARCI?A A J， IRIBARNE L， CORRAL A， et al. A recommender system for component-based applications using machine learning techniques［J］. Knowledge-Based Systems， 2019，164： 68－84.
6 XIAO B， BENBASAT I. E-commerce product recommendation agents： Use， characteristics， and impact［J］. MIS Quarterly， 2007， 31（1）：137－209.
7 王琳，郑长娟，彭新敏. 国外企业-顾客合作创新研究述评与展望［J ］.外国经济与管理，2012， 34（9）：66－72.
8 KANKANHALLI A， YE H， TEO H H. Comparing Potential and Actual Innovators［J］. MIS Quarterly， 2015， 39（3）：667－682.
9 JEPPESEN L B， FREDERIKSEN L. Why do users contribute to firm-hosted user communities？ The case of computer-controlled music instruments［J］. Organization science， 2006， 17（1）： 45－63.
10 谢康，吴记，肖静华.基于大数据平台的用户画像与用户行为分析［J］.中国信息化，2018，（3）：100－104.
11 于飞，蔡翔，董亮.研发模式对企业创新的影响——知识基础的调节作用［J］.管理科学，2017， 30（3）：97－109.
12 ADOMAVICIUS G， TUZHILIN A. Toward the next generation of recommender systems： A survey of the state-of-the-art and possible extensions［J］. IEEE transactions on knowledge and data engineering， 2005， 17（6）： 734－749.
13 KNIJNENBURG B P， WILLEMSEN M C， GANTNER Z， et al. Explaining the user experience of recommender systems. User Model［J］. User-Adapted Interaction， 2012， 22（4-5）： 441－504.
14 LI X， GRAHL J， HINZ O. How do recommender systems lead to consumer purchases？ A causal mediation analysis of a field experiment［J］. Information Systems Research， 2022， 33（2）： 620－637.
15 PU P， CHEN L， HU R. A user-centric evaluation framework for recommender systems［C］// Proceedings of the fifth ACM conference on Recommender systems.Chicago：RecSys，2011： 157－164.
16 MCNEE S M， RIEDL J， KONSTAN J A. Being accurate is not enough： how accuracy metrics have hurt recommender systems［C］// CHI'06 extended abstracts on Human factors in computing systems. 2006： 1097－1101.
17 PARAPAR J， RADLINSKI F. Towards Unified Metrics for Accuracy and Diversity for Recommender Systems［C］. 15th ACM Conference on Recommender Systems. ACM. 2021： 75－84.
18 ZIEGLER C N， MCNEE S M， KONSTAN J A， et al. Improving recommendation lists through topic diversification［C］//Proceedings of the 14th International Conference on World Wide Web. Seoul：ACM.2005： 22－32.
19 WILLEMSEN M C， KNIJNENBURG B P， GRAUS M P， et al.Using latent features diversification to reduce choice difficulty in recommendation lists［C］// RecSys 11 Workshop on Human Decision Making in Recommender Systems. Chicago：CEUR-WS， 2011： 14－20.
20 KOMIAK S Y X， BENBASAT I. The effects of personalization and familiarity on trust and adoption of recommendation agents［J］. MIS quarterly， 2006： 941－960.
21 罗婷予.基于用户体验的数字记忆资源推荐策略研究［D］.广州：中山大学信息管理学院，2022.