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| Mining User Preferences in Crowdsourcing Community with Sensitivity Analysis |
Zhang Tingting1( ), Zhao Yuxiang2, Zhu Qinghua3 |
1School of Engineering and Management, Nanjing University, Nanjing 210093, China
2School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
3School of Information Management, Nanjing University, Nanjing 210093, China |
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Abstract [Objective] This paper analyzes the attributes and task characteristics of crowdsourcing community users, aiming to identify their potential interests or preferences. [Methods] First, we studied the user’s sensitivity to task attributes based on sensitivity analysis method, and constructed a model for mining user’s potential preferences with bipartite graph. Then, we used this model to discover the implicit preferences from user’s behaviors. Finally, we confirmed the validity of the proposed model through experimental analysis. [Results] Our model could effectively identify the degrees of users’ sensitivity to Books, Software, and Music etc. It could also discover users’ potential interests or preferences to Pyrex Oblong Roaster, Oxford, and Cashback etc. to predict their choices. Compared with traditional collaborative filtering algorithms, the proposed model has a smaller MAE value. [Limitations] Our preferences mining model is based on users in the competitive environment, and it does not consider the complementarity among the interests of collaborative users. [Conclusions] The proposed model could accurately understand the users’ interests in crowdsourcing community, and then reveal their potential preferences. It helps us effectively distribute crowdsourcing tasks.
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Received: 05 December 2017
Published: 20 June 2018
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