Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

TL;DR

This paper introduces TMCDR, a transfer-meta learning framework that enhances cross-domain recommendation for cold-start users by improving generalization through meta learning, outperforming existing methods across multiple datasets.

Contribution

The paper proposes a novel transfer-meta framework for cross-domain recommendation that improves generalization to cold-start users by combining transfer learning and meta learning, applicable to various base models.

Findings

TMCDR outperforms existing CDR methods on six tasks.

The framework demonstrates strong compatibility with different base models.

Extensive experiments validate the effectiveness of TMCDR across datasets.

Abstract

Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary (source) domain to improve the performance of recommender system in the target domain. In these CDR approaches, the family of Embedding and Mapping methods for CDR (EMCDR) is very effective, which explicitly learn a mapping function from source embeddings to target embeddings with overlapping users. However, these approaches suffer from one serious problem: the mapping function is only learned on limited overlapping users, and the function would be biased to the limited overlapping users, which leads to unsatisfying generalization ability and degrades the performance on cold-start users in the target domain. With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage. In the transfer (pre-training) stage, a source model and a target model are trained on source and target domains, respectively. In the meta stage, a task-oriented meta network is learned to implicitly transform the user embedding in the source domain to the target feature space. In addition, the TMCDR is a general framework that can be applied upon various base models, e.g., MF, BPR, CML. By utilizing data from Amazon and Douban, we conduct extensive experiments on 6 cross-domain tasks to demonstrate the superior performance and compatibility of TMCDR.