Examining representation, classification, and personalization using a unified framework

Information retrieval and filtering (IRF) in dynamic environments such as the WWW poses many challenges. At the root of the challenges lie the twin factors of data diversity and data evolution. These two factors are especially problematic for content representation and classification. With a growing number of people relying on web search applications to support everyday tasks, another emerging challenge is providing highly focused results (i.e., personalization) while accommodating diverse user-interests and interest drifts. In fact, the key challenges associated with representation, classification, and personalization are interdependent and the likelihood of achieving progress in any one of these areas can be increased by focusing on them together – as part of a single information services framework.

In this talk, I will present and discuss such a framework, known as the multilevel information services model. I will demonstrate how the modular nature of the framework permits the isolation and study of key functions that are more basic than IRF and establish their individual and combined impact on the effectiveness and efficiency of information services. I will also present results from a series of experiments examining how factors such as the representation method (e.g., automated feature generation vs. manual schemes), classifiers (e.g., supervised vs. unsupervised), nature and size of content streams, level of user interaction, and rate of interest change influence system performance.