A recommendation framework towards personalized services in intelligent museum

Shandan Zhou, Xingshe Zhou, Zhiwen Yu, Kaibo Wang, Haipeng Wang, Hongbo Ni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Museum visitors are being overloaded with increasing amount and variety of information that heavens their burden to locate what is really interesting. Development of personalized service for museum visitors makes a promising effort to alleviate the problem. In this paper, a recommendation framework and the related algorithms are proposed for intelligent museum. Using both the explicit and implicit visit behaviors data, preference learning algorithm computes the preference of a visitor in exhibits. Exhibit recommendation algorithm takes a visitor's preference and the public evaluation history on exhibits into account in the pre-selection and refinement of recommended exhibits. We implemented the recommendation framework based on our previously developed smart museum platform, iMuseum. The effectiveness of the proposed framework and algorithms are verified through experiments.

Original languageEnglish
Title of host publicationProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 - 7th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2009
Pages229-236
Number of pages8
DOIs
StatePublished - 2009
Event7th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2009 - Vancouver, BC, Canada
Duration: 29 Aug 200931 Aug 2009

Publication series

NameProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009
Volume2

Conference

Conference7th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2009
Country/TerritoryCanada
CityVancouver, BC
Period29/08/0931/08/09

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