| Abstract: | Most Web search engines determine the relevancy of Web pages based on query terms, and a few content filtering applications allow consumers to block objectionable material. However, not many Web search engines and content filtering applications learn the user preferences over time. In this study, we proposed two machine-learning approaches that can be used to learn consumer preferences to identify documents that are most relevant to the consumer. We test the proposed machine learning approaches on a few simulated data sets. The results of our study illustrate that data mining approaches can be used to design intelligent adaptive agents that can select the relevant Web pages, given query terms, for the user. |
