A model for hypertext-based information retrieval
Hypertext: concepts, systems and applications
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
I3: A New Approach to the Design of Document Retrieval System
I3: A New Approach to the Design of Document Retrieval System
The Journal of Machine Learning Research
The perfect search engine is not enough: a study of orienteering behavior in directed search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Find-similar: similarity browsing as a search tool
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Towards task-based personal information management evaluations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
What to do when search fails: finding information by association
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Exploratory Search
Improving search engines using human computation games
Proceedings of the 18th ACM conference on Information and knowledge management
iMecho: an associative memory based desktop search system
Proceedings of the 18th ACM conference on Information and knowledge management
Ranking using multiple document types in desktop search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Building a semantic representation for personal information
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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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.