NDLI: Supporting exploratory people search: a study of factor transparency and user control

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Scholarly big data: information extraction and data mining

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Supporting exploratory people search: a study of factor transparency and user control

Content Provider	ACM Digital Library
Author	He, Daqing Jiang, Jiepu Han, Shuguang Yue, Zhen
Abstract	People search is an active research topic in recent years. Related works includes expert finding, collaborator recommendation, link prediction and social matching. However, the diverse objectives and exploratory nature of those tasks make it difficult to develop a flexible method for people search that works for every task. In this project, we developed PeopleExplorer, an interactive people search system to support exploratory search tasks when looking for people. In the system, users could specify their task objectives by selecting and adjusting key criteria. Three criteria were considered: the content relevance, the candidate authoritativeness and the social similarity between the user and the candidates. This project represents a first attempt to add transparency to exploratory people search, and to give users full control over the search process. The system was evaluated through an experiment with 24 participants undertaking four different tasks. The results show that with comparable time and effort, users of our system performed significantly better in their people search tasks than those using the baseline system. Users of our system also exhibited many unique behaviors in query reformulation and candidate selection. We found that users' general perceptions about three criteria varied during different tasks, which confirms our assumptions regarding modeling task difference and user variance in people search systems.
Starting Page	449
Ending Page	458
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505684
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	People search Exploratory search Recommender system Interactive information retrieval
Content Type	Text
Resource Type	Article

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