Papers by Keyword: User Feedback

Abstract: Metasearch engine is a system that provides unified access to multiple existing search engines. After the results returned from all used component search engines are collected, the metasearch system merges the results into a single ranked list which is expected to be better than the results of the best of the participating search systems. The success of a metasearch engine depends mainly on their rank aggregation method. The system is a better one, if the aggregated list of results displayed before the user satisfies the user with his information need. In this paper, we discuss the development of a metasearch engine that performs user feedback based metasearching using modified rough set based aggregation. Metasearching using the modified rough set based aggregation is performed in two phases namely the ranking rule learning phase and the rank aggregation phase. For each query in the training set, we mine the ranking rules and select the best rules-set by performing cross-validation test. Once the system is trained, we use the best rule set to get the overall ranking for the results returned from different search systems in response to other queries. We also present few snapshots of our system.

Abstract: This paper presents the empirical results from experiment on speed-accuracy trade-off in ‘drag and drop’ movements. Although the previous work has modeled a theorem, framework and achievement, but some researcher focused only on the system error. In this paper, we design an analysis mechanism with the software that could behave as actual computer in producing error (system bug). This study aims at investigating the user anticipation in multiple modalities in relation to users’ performance. The multiple modalities represent four feedbacks; default, audio, text and visual that integrate with error enforcement forcing error with certain percentage to simulate system error and actual computer behavior. The finding shows the average means of 4.0667 for feedback modality of which, we conclude that the users anticipate with feedback modality can reacts to improve their performance in movement mechanism.