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Tuesday, April 25, 2017

Reading 15: User-Driven System-Mediated Collaborative Information Retrieval

Motivation: Collaborative information retrieval (CIR) involves more than one information seekers in a searching process. Different from individual information retrieval (IIR), CIR needs to take into account users' skills and preferences, also to support their communication and cooperations. Existing approaches can be generally divided into two categories. One is user-based CIR system which allows users to decide role assignments and supports their communication in searching process; the other one is system-based CIR which system would impose roles to users and optimizes information retrieval. Both of them have limitations and merits. This paper tries to combine them together, and proposes a user-driven system-mediated CIR system.

Approach: In the user-driven system-mediated CIR, users' searching behaviors would be monitored and analyzed; it would discover significant behavioral differences between each pair of users; finally it suggests roles to users in order to leverage the best of users' skills and preferences. Take the pair of roles "Gatherer versus Surveyor" as an example. In a collaborative searching process, gatherer would be more likely to seek highly relevant documents, so that he or she would perform queries with much overlap, and spend more time reading webpage contents; whereas, surveyor would tend to explore new things on websites, he or she would try different queries, spend less them on each webpage and accordingly, the query success is low. By analyzing such features, i.e. query overlap, dwell time and query success, we can obtain insights into user's role difference.

Experiments: They ask participants (students in Rutgers University) to collaboratively write a report on an exploratory topic. User study #1 has the topic of "Gulf oil spill" and user study #2 focuses on the topic of "Global warming". In the searching session, supportive chat system and search tools enable bookmarking webpages and saving snippets. Specifically, three types of features are considered (shown in Table 1).
Table 1. Features used to describe a searching session.
By analyzing users' searching behaviors, they found that significant behavioral difference became obvious after a short period of time as a session started. Figure 1 reveals this observation as p values are very significant. Besides, during the same session, users do not change their roles.
Figure 1. Significant difference in users' search behaviors.
Finally, they compare the effect of role mining on information retrieval in a CIR task. Table 2 shows the comparison with four baselines and report the average increase that RB-CIR has obtained. From it we observe that, RB-CIR outperforms all other methods except for PM-CIR. They argued that since the difference between RB-CIR and PM-CIR is not significant, it's still reasonable to say that RB-CIR has better performance.
Table 2. Comparison of RB-CIR with four baselines.
One limitation in their proposed method is the lacking of prior knowledge of users' skills as roles are mined from current searching behaviors. Therefore, in the further they would consider users' prior searching behaviors and preferences to complement current actions.

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