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CLHE, the Contrastive Learning-enhanced Hierarchical Encoder, emerges as a game-changer in bundle construction. Overcoming challenges in learning expressive representations and handling modality issues, CLHE outperforms leading methods. The paper highlights its success and points to future directions, urging exploration in flexible evaluation settings, optimizing feature extractors, and venturing into personalized bundle construction.

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