Generative Ranking based Sequential Recommendation in Software Crowdsourcing
Weisong Sun, Xuefeng Yan, Arif Ali Khan · 2020
The sequential recommendation system predicts user's future operations based on their historical interaction information and achieves good performance in recent work. However, when applying to the task of recommendation in software crowdsourcing platform, the accuracy of the previous recommendation models is significantly reduced because of the sparse interactive data and dynamic item list in the platform. The Generative Ranking based Sequential Recommendation Model (GRS) is proposed to solve the problems mentioned above. The generative layer is introduced into a translation-based recommendation model to prevent overfitting problem. By generating latent vector in feature space, the interpolation between encoded points is highly reduced and the model is adapted to achieve better performance by embedding auxiliary features into the model. The efficiency and feasibility of the model is validated by the experiment in different datasets extracted from crowdsourcing platforms.