Transfer learning for sequential recommendation model

Abstract

In this work, we address the problem of transfer learning for sequential recommendation model. Most of the state-of-the-art recommendation systems consider user preference and give customized results to different users. However, for those users without enough data, personalized recommendation systems cannot infer their preferences well or 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. Most of recommendation systems combined with transfer learning aim at the rating prediction problem whose user feedback is explicit and not sequential. In this paper, we apply transfer learning techniques to a model utilizing user preference and sequential information. To the best of our knowledge, no previous works have addressed the problem. Experiments on realworld datasets are conducted to demonstrate our framework is able to improve prediction accuracy by utilizing auxiliary data.