Journal Home > Volume 1 , Issue 2
Purpose

The purpose of this paper is to study the architecture of holographic personalized portal, user modeling, commodity modeling and intelligent interaction.

Design/methodology/approach

In this paper, the authors propose crowd-science industrial ecological system based on holographic personalized portal and its interaction. The holographic personality portal is based on holographic enterprises, commodities and consumers, and the personalized portal consists of accurate ontology, reliable supply, intelligent demand and smart cyberspace.

Findings

The personalized portal can realize the information acquisition, characteristic analysis and holographic presentation. Then, the intelligent interaction, e.g. demand decomposition, personalized search, personalized presentation and demand prediction, will be implemented within the personalized portal.

Originality/value

The authors believe that their work on intelligent interaction based on holographic personalized portal, which has been first proposed in this paper, is innovation focusing on the interaction between intelligence and convenience.


menu
Abstract
Full text
Outline
About this article

Intelligent interaction based on holographic personalized portal

Show Author's information Yadong HuangYueting Chai( )Yi LiuXiang Gu
Department of Automation, National Engineering Laboratory for E-Commerce Technologies, Tsinghua University, Beijing, China

Abstract

Purpose

The purpose of this paper is to study the architecture of holographic personalized portal, user modeling, commodity modeling and intelligent interaction.

Design/methodology/approach

In this paper, the authors propose crowd-science industrial ecological system based on holographic personalized portal and its interaction. The holographic personality portal is based on holographic enterprises, commodities and consumers, and the personalized portal consists of accurate ontology, reliable supply, intelligent demand and smart cyberspace.

Findings

The personalized portal can realize the information acquisition, characteristic analysis and holographic presentation. Then, the intelligent interaction, e.g. demand decomposition, personalized search, personalized presentation and demand prediction, will be implemented within the personalized portal.

Originality/value

The authors believe that their work on intelligent interaction based on holographic personalized portal, which has been first proposed in this paper, is innovation focusing on the interaction between intelligence and convenience.

Keywords: E-commerce, Personalized portal, User modelling, Holographic information, Intelligent interaction

References(13)

Adomavicius, G., Sankaranarayanan, R. and Sen, S. (2005), “Incorporating contextual information in recommender systems using a multidimensional approach”, ACM Transactions on Information Systems, Vol. 23 No. 1, pp. 103-145.

Aggarwal, C.C. and Yu, P.S. (2000), “Data mining techniques for personalization”, Data Engineering Bulletin, Vol. 23, pp. 4-9.

Bruyn, A.D., Liechty, J.C. and Huizingh, E.K.R.E. (2008), “Offering online recommendations with minimum customer input through conjoint-based decision aids”, Marketing Science, Vol. 27 No. 3, pp. 443-460.

Chuan, N.K., Sivaji, A. and Shahimin, M.M. (2013), “Kansei engineering for e-commerce sunglasses selection in Malaysia”, Procedia-Social and Behavioral Sciences, Vol. 97 No. 2, pp. 707-714.

Claypool, M., Le, P. and Wased, M. (2001), “Implicit interest indicators”, Proceedings of the 6th International Conference on Intelligent User Interfaces, ACM, pp. 33-40.https://doi.org/10.1145/359784.359836
DOI
Joachims, T., Freitag, D. and Mitchell, T. (1997). “WebWatcher: a tour guide for the world wide web”, Proceedings of the Ijcai.

Lomax, S. and Vadera, S. (2013), “A survey of cost-sensitive decision tree induction algorithms”, ACM Computing Surveys, Vol. 45 No. 2, pp. 1-35.

Koren, Y. (2009), “Collaborative filtering with temporal dynamics”, ACM, Vol. 53, pp. 447-456.

Santos, T.R.L.D. and Zárate, L.E. (2015), “Categorical data clustering: What similarity measure to recommend”, Expert Systems with Applications, Vol. 42 No. 3, pp. 1247-1260.

Smyth, B., Bradley, K. and Rafter, R. (2002), “Personalization techniques for online recruitment services”, Communications of the ACM, Vol. 45 No. 5, pp. 39-40.

Williams, T., Roger, M. and Bruce, E. (2002), “Demand chain management theory: constraints and development from global aerospace supply webs”,Journal of Operations Management, Vol. 20 No. 6, pp. 691-706.

Xu, Z. (2006), “Multiple attribute decision making based on different types of linguistic information”, Journal of Southeast University, Vol. 22, pp. 134-136.

Ying, X. (2003), The Research on User Modeling for Internet Personalized Services, National University of Defense Technology.
Publication history
Copyright
Rights and permissions

Publication history

Received: 16 August 2017
Revised: 27 August 2017
Accepted: 28 August 2017
Published: 12 June 2017
Issue date: June 2017

Copyright

© The author(s)

Rights and permissions

Yadong Huang, Yueting Chai, Yi Liu and Xiang Gu. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Return