A unified framework for cross-domain and cross-system recommendations

Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, Guanfeng Liu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help of a source one (with relatively richer information). However, most existing CDR and CSR approaches are single-target, which can only help the target dataset and thus cannot benefit the source dataset. In this paper, we focus on three new scenarios, i.e., Dual-Target CDR (DTCDR), Multi-Target CDR (MTCDR), and CDR+CSR, and aim to improve the recommendation accuracy in all datasets simultaneously. To do this, we propose a unified framework, called GA (based on Graph embedding and Attention techniques), for all three scenarios. In GA, we first construct separate heterogeneous graphs to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common entities (users/items) learned from different datasets. Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P for the three scenarios respectively. Extensive experiments conducted on four real-world datasets demonstrate that our proposed GA models significantly outperform the state-of-the-art approaches.

Original languageEnglish
JournalIEEE Transactions on Knowledge and Data Engineering
Early online date16 Aug 2021
DOIs
Publication statusE-pub ahead of print - 16 Aug 2021

Bibliographical note

Publisher Copyright:
IEEE

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Business process re-engineering
  • Collaborative filtering
  • Cross-Domain Recommendation
  • Cross-System Recommendation
  • Motion pictures
  • Recommender systems
  • Recommender Systems
  • Social networking (online)
  • Training
  • Transfer learning

Fingerprint

Dive into the research topics of 'A unified framework for cross-domain and cross-system recommendations'. Together they form a unique fingerprint.

Cite this