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An FM Developer Recommendation Algorithm by Considering Explicit Information and ID Information

机译：通过考虑显式信息和ID信息，FM开发人员推荐算法

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摘要

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 Top-coder platform show that the proposed algorithm has obvious advantages over the comparison algorithm in precision and recall.
机译：最近，由于越来越大的任务和开发人员聚集在平台上，众群软件平台的开发商建议具有很大的研究意义。为了解决冷启停的问题，现有的开发人员推荐算法通常只使用明确的信息但不是ID信息来表示任务和开发人员，这会导致性能不佳。鉴于现有开发人员推荐算法的缺点，本文提出了一种基于明确的隐式特征映射关系建模的FM推荐算法。该算法首先通过使用FM算法将完成的任务和现有开发人员之间完全ID信息，显式信息和额定级交互集成，以便获得与其ID信息相关的隐式功能。其次，对于完成任务和现有开发人员，建立了一个深度回归模型，以了解从显式功能到隐式功能的映射关系。然后，对于冷启动任务或冷启动开发人员，根据深度回归模型，由显式特征确定隐式特征。最后，可以通过训练的FM模型预测冷启动场景中的额定值，具有显式和隐式功能。顶部编码器平台上的仿真结果表明，该算法在精度和召回中的比较算法方面具有明显的优势。

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