Bayesian Graphical Models for Adaptive Filtering (2005)
Abstract:
A personal information filtering system monitors an incoming document stream to find the documents that match information needs specified by user profiles. The most challenging aspect in adaptive filtering is to develop a system to learn user profiles efficiently and effectively from very limited user supervision. In order to overcome this challenge, the system needs to do the following: use a robust learning algorithm that can work reasonably well when the amount of training data is small and be more effective with more training data; explore what a user likes while satisfying the user’s immediate information need and trade off exploration and exploitation; consider many aspects of a document besides relevance, such as novelty, readability and authority; use multiple forms of evidence, such as user context and implicit feedback from the user, while interacting with a user; and handle various scenarios, such as missing data, in an operational environment robustly. This dissertation uses the Bayesian graphical modelling approach as a unified framework for filtering. We customize the framework to the filtering domain and develop a set of solutions that enable us to build a filtering system with the desired characteristics in a principled way. We evaluate and justify these solutions on a large and diverse set of standard and new adaptive filtering test collections. Firstly, we develop
Citations
| 45 | Novelty and Redundancy Detection in Adaptive Filtering – Zhang, Callan, et al. - 2002 |
| 26 | Maximum likelihood estimation for filtering thresholds – Zhang, Callan - 2001 |
| 5 | The Bias Problem and Language Models in Adaptive Filtering – Zhang, Callan - 2001 |
| 5 | Exploration and exploitation in adaptive filtering based on Bayesian active learning – Zhang, Xu, et al. - 2003 |
