Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2022
Abstract
Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively. However, they either use a unified view without differentiation or loosely combine the predictions of two separate views, while the crucial cooperative association between the two views' representations is overlooked.In this work, we propose to model the cooperative association between the two different views through cross-view contrastive learning. By encouraging the alignment of the two separately learned views, each view can distill complementary information from the other view, achieving mutual enhancement. Moreover, by enlarging the dispersion of different users/bundles, the self-discrimination of representations is enhanced. Extensive experiments on three public datasets demonstrate that our method outperforms SOTA baselines by a large margin. Meanwhile, our method requires minimal parameters of three set of embeddings (user, bundle, and item) and the computational costs are largely reduced due to more concise graph structure and graph learning module. In addition, various ablation and model studies demystify the working mechanism and justify our hypothesis. Codes and datasets are available at https://github.com/mysbupt/CrossCBR.
Keywords
bundle recommendation, contrastive learning, graph neural network
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, USA, August 14-18
First Page
1233
Last Page
1241
Identifier
10.1145/3534678.3539229
Publisher
ACM
City or Country
New York
Citation
MA, Yunshan; HE, Yingzhi; ZHANG, An; WANG, Xiang; and CHUA, Tat-Seng.
CrossCBR: Cross‑view contrastive learning for bundle recommendation. (2022). KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, USA, August 14-18. 1233-1241.
Available at: https://ink.library.smu.edu.sg/sis_research/10866
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/10.1145/3534678.3539229