A framework of online shopping support for information recommendations

Wen Shan Lin, Nathalie Cassaigne, Tzung Cheng Huan

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


The growth of e-commerce has caused problems with personalized recommendations. Although several attempts have made to improve or automate the retrieval and filtering of such information, no generic framework links the semantic context of online shopping with shoppers' purchases in order to improve the efficiency of online shopping support. Through the application of knowledge-modeling, this paper selects a college population to empirically investigate and establish the relationship between e-marketing terms and shoppers' buying behavior. General online shopping and the online book purchases are selected to validate the generic framework. Two hypotheses are tested: (1) e-marketing terms are important in influencing shoppers' decisions; and (2) shoppers behave differently with respect to different types of buys. Experimental results indicate that shoppers perceive the importance of e-marketing terms differently whilst shopping online. Six types of shoppers' are classified: (1) general-purpose, (2) securityconcerned, (3) value, (4) fashionable, (5) time-sensitive, and (6) service-oriented. Results and future research opportunities are discussed. This paper serves as a basis for improving online information search for shopping purposes.

Original languageEnglish
Pages (from-to)6874-6884
Number of pages11
JournalExpert Systems With Applications
Issue number10
Publication statusPublished - 2010 Jan 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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