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CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation | | Yunshan Ma
; Yingzhi He
; An Zhang
; Xiang Wang
; Tat-Seng Chua
; | | Date: |
1 Jun 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 this https URL | | Source: | arXiv, 2206.00242 | | Services: | Forum | Review | PDF | Favorites | |
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