Transfer Learning for Sequential Recommendation Model

In this work, we attempt to apply transfer learning to sequential recommendation model. Most of the state-of-the-art recommendation systems consider user preference and give different results to different users. However, for those users without enough data, personalized recommendation systems cannot infer their preference well and then rank items precisely. Recently, transfer learning techniques are applied to address this problem. Although the lack of data in target domain may result in underfitting, data from auxiliary domains can be utilized to assist model training. However, most of recommendation systems combined with transfer learning aim at rating-based problems whose user feedback is explicit and not sequential. In this paper, we want to apply transfer learning techniques to a model utilizing user preference and sequential information. Experiments on real-world dataset are conducted to demonstrate the effectiveness of our framework.