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Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation

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Yinliang YueAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 2806 - 2816

https://doi.org/10.1145/3511808.3557472

Published: 17 October 2022 Publication History

Abstract

Recommender systems play a significant role in online services and have attracted wide attention from both academia and industry. In this paper, we focus on an important, practical, but often overlooked task: domain-level zero-shot recommendation (DZSR). The challenge of DZSR mainly lies in the absence of collaborative behaviors in the target domain, which may be caused by various reasons, such as the domain being newly launched without existing user-item interactions, or users' behaviors being too sensitive to collect for training. To address this challenge, we propose a Transferable Interest Graph Embedding technique for Recommendations (Tiger). The key idea is to connect isolated collaborative filtering datasets with a knowledge graph tailored to recommendations, then propagate collaborative signals from public domains to the zero-shot target domain. The backbone of Tiger is the transferable interest extractor, which is a simple yet effective graph convolutional network (GCN) aggregating multiple hops of neighbors on a shared interest graph. We find that the bottom layers of GCN preserve more domain-specific information while the upper layers represent universal interest better. Thus, in Tiger, we discard the bottom layers of GCN to reconstruct user interest so that collaborative signals can be successfully propagated to other domains, and retain the bottom layers of GCN to include domain-specific information for items. Extensive experiments with four public datasets demonstrate that Tiger can effectively make recommendations for a zero-shot domain and outperform several alternative baselines.

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Cited By

Hu BWang WWang SLiu ZShen BHe YChen JSerra ESpezzano F(2024)The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck PerspectiveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679595(880-889)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679595
Li BQuan HWang JLiu PCai HMiao YYang YLi L(2024)Neural Library Recommendation by Embedding Project-Library Knowledge GraphIEEE Transactions on Software Engineering10.1109/TSE.2024.339350450:6(1620-1638)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3393504
Jiang JZhao HHe MWu LZhang KFan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614945(965-975)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614945
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Index Terms

Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation
1. Information systems

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

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Published: 17 October 2022

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Hu BWang WWang SLiu ZShen BHe YChen JSerra ESpezzano F(2024)The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck PerspectiveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679595(880-889)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679595
Li BQuan HWang JLiu PCai HMiao YYang YLi L(2024)Neural Library Recommendation by Embedding Project-Library Knowledge GraphIEEE Transactions on Software Engineering10.1109/TSE.2024.339350450:6(1620-1638)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3393504
Jiang JZhao HHe MWu LZhang KFan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614945(965-975)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614945
Gao WWang HLiu QWang FLin XYue LZhang ZLv RWang SChen HDuh WHuang HKato MMothe JPoblete B(2023)Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive DiagnosisProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591774(983-992)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591774

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