Personalized web content provider recommendation through mining individual users' QoS

  • Authors:

  • Songhua Xu;Hao Jiang;Francis C. M. Lau

  • Affiliations:

  • Zhejiang University, Hangzhou, Zhejiang, P.R. China and Yale University, New Haven, Connecticut and The University of Hong Kong, Hong Kong S.A.R., P.R. China;The University of Hong Kong, Hong Kong S.A.R., P.R. China;The University of Hong Kong, Hong Kong S.A.R., P.R. China

  • Venue:
  • Proceedings of the 11th International Conference on Electronic Commerce
  • Year:
  • 2009

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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.