论文标题
估计对话推荐系统的培训成本
On Estimating the Training Cost of Conversational Recommendation Systems
论文作者
论文摘要
对话推荐系统最近引起了很多关注，因为用户可以在多个对话转弯中与系统不断互动。但是，会话推荐系统基于复杂的神经体系结构，因此此类模型的训练成本很高。为了阐明最先进的对话模型的高计算训练时间，我们研究了五种代表性策略并证明了这个问题。此外，我们讨论了在知识蒸馏策略之后应对高训练成本的可能方法，我们详细介绍了减少对话推荐系统中大量模型参数的在线推理时间的关键挑战
Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns. However, conversational recommendation systems are based on complex neural architectures, thus the training cost of such models is high. To shed light on the high computational training time of state-of-the art conversational models, we examine five representative strategies and demonstrate this issue. Furthermore, we discuss possible ways to cope with the high training cost following knowledge distillation strategies, where we detail the key challenges to reduce the online inference time of the high number of model parameters in conversational recommendation systems
