@article{4200,
  author = {Essam Natsheh},
  title = {Personalized Web Documents Filtering by Analyzing User Browsing Behaviors},
  journal = {International Journal of Information Studies},
  year = {2000},
  volume = {5},
  number = {2},
  doi = {},
  url = {https://www.dline.info/ijis/fulltext/v5n2/ijisv5n2_2.pdf},
  abstract = {This paper describes a method for information filtering in web browsing to learn user’s preferences. The
purposed method observes user’s reactions to the filtered documents and learns from them the profiles for the individual users.
Reinforcement learning is used to adapt the most significant terms that best represent user’s interests. In contrast to conventional
relevance feedback methods which require explicit user feedbacks, our approach learns user preference implicitly from
direct observations of browsing behavior during interaction. We made field tests that involved number of users and number
of document during period of time, to show if our methods have the superior performance in personalized information
filtering.},
}