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EMIT University at TREE 2008: Relevance Feedback Track Johannes Sega Fall Scholar Simon Publish School of Computer Science and IT EMIT University, GPO Box 2476V Melbourne 3001, Australia 1 Introduction

If a result is retrieved, the feedback allows the user to decide whether they want to proceed with the result even if the retrieval is poor. 2 Data Collection The data for this paper were collected during a two-month time period in October 2008 during the last two weeks of September and October. At each weekend participants provided feedback on the relevance of retrieved links. A random sample of 12 participants provided feedback of more than 1 item on each of the 8 search pages for different items. Each participant was assessed by an independent tester. These results form the basis for subsequent discussion and interpretation of the results. The tester was a student in an engineering project. He collected information from the participant's email, answering all relevant questions about the content of the email. 3 Summary As with all such results, the overall picture should be considered in context. It may become apparent that the data is a little skewed when one considers that the participant used an email-based system for feedback. Nevertheless, the overall point is clear: in general, the relevance feedback process is a valid one. The main point of relevance feedback is that it increases a system's capacity to deal with bad results, making the system more robust to misclassifications of items or retrieval errors. This should give users confidence that the content returned by a system is relevant to their search need. The next part of the report will concentrate on the specific results of relevance feedback at EMIT University in Melbourne. The report ends with a discussion on the application of relevance feedback to other relevance issues. Previous Section Next Section A New Approach to the Relevance Feedback Problem To understand the relevance feedback issue in greater depth and apply a new perspective for designing relevance feedback systems, we studied the system for retrieving text from online text databases, such as the web, the web 2.0, and Amazon's Mechanical Turk (MTurk.com). We refer to this system as MTurk-LINK (). We have developed a simplified version of the system. The full system, including a data model and an error recovery algorithm, is presented in Appendix S1. To provide an overview of how the system works in more depth, we provide the following examples. A user enters a query into the search box with a text box next to it. The user has the option of entering some text on the search page that will also appear on the search results page. For example, the user may type a date and see results for that day.

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