Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2020
Abstract
Cold-start recommendation has been a challenging problem due to sparse user-item interactions for new users or items. Existing efforts have alleviated the cold-start issue to some extent, most of which approach the problem at the data level. Earlier methods often incorporate auxiliary data as user or item features, while more recent methods leverage heterogeneous information networks (HIN) to capture richer semantics via higher-order graph structures. On the other hand, recent meta-learning paradigm sheds light on addressing cold-start recommendation at the model level, given its ability to rapidly adapt to new tasks with scarce labeled data, or in the context of cold-start recommendation, new users and items with very few interactions. Thus, we are inspired to develop a novel meta-learning approach named MetaHIN to address cold-start recommendation on HINs, to exploit the power of meta-learning at the model level and HINs at the data level simultaneously. The solution is non-trivial, for how to capture HIN-based semantics in the metalearning setting, and how to learn the general knowledge that can be easily adapted to multifaceted semantics, remain open questions. In MetaHIN, we propose a novel semantic-enhanced tasks constructor and a co-adaptation meta-learner to address the two questions. Extensive experiments demonstrate that MetaHIN significantly outperforms the state of the arts in various cold-start scenarios. (Code and dataset are available at https://github.com/rootlu/MetaHIN.)
Keywords
Heterogeneous Information Network, Meta-learning, Cold-start Recommendation
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Information Systems and Management
Publication
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Diego, CA, August 22-27
First Page
1563
Last Page
1573
ISBN
9781450379984
Identifier
10.1145/3394486.3403207
Publisher
ACM
City or Country
New York
Citation
LU, Yuanfu; FANG, Yuan; and SHI, Chuan.
Meta-learning on heterogeneous information networks for cold-start recommendation. (2020). KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Diego, CA, August 22-27. 1563-1573.
Available at: https://ink.library.smu.edu.sg/sis_research/5155
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3394486.3403207