Personalized web content provider recommendation through mining individual users' QoS

Songhua Xu, Hao Jiang, Francis C.M. Lau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose an optimal web content provider recommendation algorithm based on mining QoS (quality of service) information of the Internet. The QoS refers principally to the network bandwidth and waiting time (for a connection to be established). For contents replicated over multiple sites, our algorithm recommends a list of webpages having the desired content and ranked according to their QoSs for any specific user. The recommendation is generated through a data mining procedure based on known QoSs of connections between pairs of computers. Our user QoS mining procedure incrementally constructs a neural network group for QoS prediction based on clustering over the prediction errors. An accompanying decision tree algorithm is then used to select the most appropriate neural network among the neural network group to predict the QoS for a particular user connection. Based on our proposed recommendation algorithm, we have implemented a user-oriented search engine which can identify similar web content providers and make a ranked recommendation based on the prediction over the QoS experienced by individual users. Experiment results have verified that our QoS-based personal web content provider ranking algorithm can indeed produce a recommendation that improves the QoS experienced by individual users.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th International Conference on Electronic Commerce, ICEC 2009
Pages89-98
Number of pages10
DOIs
StatePublished - 2009
Event11th International Conference on Electronic Commerce, ICEC 2009 - Taipei, Taiwan, Province of China
Duration: Aug 12 2009Aug 15 2009

Publication series

NameACM International Conference Proceeding Series

Other

Other11th International Conference on Electronic Commerce, ICEC 2009
Country/TerritoryTaiwan, Province of China
CityTaipei
Period8/12/098/15/09

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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