Evaluating an associative browsing model for personal information

Recent studies suggest that associative browsing can be beneficial for personal information access. Associative browsing is intuitive for the user and complements other methods of accessing personal information, such as keyword search. In our previous work, we proposed an associative browsing model of personal information in which users can navigate through the space of documents and concepts (e.g., person names, events, etc.). Our approach differs from other systems in that it presented a ranked list of associations by combining multiple measures of similarity, whose weights are improved based on click feedback from the user. In this paper, we evaluate the associative browsing model we proposed in the context of known-item finding task. We performed game-based user studies as well as a small scale instrumentation study using a prototype system that helped us to collect a large amount of usage data from the participants. Our evaluation results show that the associative browsing model can play an important role in known-item finding. We also found that the system can learn to improve suggestions for browsing with a small amount of click data.

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