A semantic-expansion approach to personalized knowledge recommendation

  • Authors:

  • Ting-Peng Liang;Yung-Fang Yang;Deng-Neng Chen;Yi-Cheng Ku

  • Affiliations:

  • Department of Information Management and Electronic Commerce Research Center, National Sun Yat-sen University, Kaohsiung, Taiwan;Electronic Commerce Research Center, Institute of Information Industries, Taipei, Taiwan;Department of Management Information Systems, National Pingtung University of Science and Technology, Taiwan;Department of Computer Science and Information Management, Providence University, Taiwan

  • Venue:
  • Decision Support Systems
  • Year:
  • 2008

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Abstract

The rapid propagation of the Internet and information technologies has changed the nature of many industries. Fast response and personalized recommendations have become natural trends for all businesses. This is particularly important for content-related products and services, such as consulting, news, and knowledge management in an organization. The digital nature of their products allows for more customized delivery over the Internet. To provide personalized services, however, a complete understanding of user profile and accurate recommendation are essential. In this paper, an Internet recommendation system that allows customized content to be suggested based on the user's browsing profile is developed. The method adopts a semantic-expansion approach to build the user profile by analyzing documents previously read by the person. Once the customer profile is constructed, personalized contents can be provided by the system. An empirical study using master theses in the National Central library in Taiwan shows that the semantic-expansion approach outperforms the traditional keyword approach in catching user interests. The proper usage of this technology can increase customer satisfaction.