Abstract: Recently, the developer recommendation on crowdsourcing software platform is of great research significance since an increasingly large number of tasks and developers have gathered on the platforms. In order to solve the problem of cold-start, the existing developer recommendation algorithms usually only use explicit information but not ID information to represent tasks and developers, which causes poor performance. In view of the shortcomings of the existing developer recommendation algorithms, this paper proposes an FM recommendation algorithm based on explicit to implicit feature mapping relationship modeling. This algorithm firstly integrates fully the ID information, explicit information and rating interaction between the completed task and the existing developers by using FM algorithm in order to get the implicit features related to their ID information. Secondly, for the completed tasks and existing developers, a deep regression model is established to learn the mapping relationship from explicit features to implicit features. Then, for the cold-start task or the cold-start developer, the implicit features are determined by the explicit features according to the deep regression model. Finally, the ratings in the cold-start scene can be predicted by the trained FM model with the explicit and implicit features. The simulation results on Topcoder platform show that the proposed algorithm has obvious advantages over the comparison algorithm in precision and recall.
Authors: Xu Yu (School of Information Science and Technology, Qingdao University of Science and Technology, China); He Ya (China); Biao Xu (China University of Mining and Technology, China); Junwei Du and Feng Jiang (Qingdao University of Science and Technology, China); Dunwei Gong (China University of Mining and Technology, China)
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