Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation

Abstract

Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem. Some efforts have been made to solve the cold-start problem by using model-agnostic meta-learning on the level of the model and heterogeneous information networks on the level of data. Moreover, using the memory-augmented meta-optimisation method effectively prevents the meta-learning model from entering the local optimum. As a result, this paper proposed memory-augmented meta-learning on meta-path, a new meta-learning method that addresses the cold-start recommendation on the meta-path furthered. The meta-path builds at the data level to enrich the relevant semantic information of the data. To achieve fast adaptation, semantic-specific memory is utilised to conduct the model with semantic parameter initialisation, and the method is optimised by a meta-optimisation method. We put this method to the test using two widely used recommended data set and three cold-start scenarios. The experimental results demonstrate the efficiency of our proposed method.

KEYWORDS:

• Cold-start recommendation
• memory-augmented
• meta-path
• meta-learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data sets of this paper is available at https://book.douban.com and https://grouplens.org/datasets/movielens/.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [Grant Number 62072170]; Youth Project of Hunan Natural Science Foundation [Grant Number 2019JJ50673]; The Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology [Grant Number 2020WLZC001]; The key research and development plan of Hunan Province [Grant Number 2022SK2109]; Scientific Research Project of Hunan Education Department [Grant Number 19C1788]; The science and technology innovation leading plan for high and new technology industries of Hunan Province [Grant Number 2020GK2029]; Science and Technology Program of Hunan Province [Grant Number 2017SK1040].