Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Yunshan Ma, Xiaohao Liu, Yinwei Wei, Zhulin Tao, Xiang Wang, Tat-Seng, Chua

TL;DR
This paper introduces CLHE, a contrastive learning-based hierarchical encoder, to improve bundle construction by effectively integrating multimodal features and user feedback, addressing data sparsity and cold-start issues.
Contribution
The paper proposes a novel CLHE framework that unifies multimodal and multi-item features using self-attention and contrastive learning for better bundle construction.
Findings
Outperforms state-of-the-art methods on four datasets
Effectively handles modality missing and noise issues
Enhances representation learning for sparse and cold-start data
Abstract
Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of existing bundles. However, it is hard to acquire large-scale well-curated bundle dataset, especially for those platforms that have not offered bundle services before. Even for platforms with mature bundle services, there are still many items that are included in few or even zero bundles, which give rise to sparsity and cold-start challenges in the bundle construction models. To tackle these issues, we target at leveraging multimodal features, item-level user feedback signals, and the bundle composition information, to achieve a comprehensive formulation of bundle construction. Nevertheless, such formulation poses two new technical challenges: 1) how to learn effective representations by optimally unifying multiple…
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Taxonomy
TopicsDigital Marketing and Social Media · Web Data Mining and Analysis
MethodsContrastive Learning
