Authors: (1) Yunshan Ma, National University of Singapore; (2) Xiaohao Liu, University of Chinese Academy of Sciences; (3) Yinwei Wei, Monash University; (4) Zhulin Tao, Communication University of China and a Corresponding author; (5) Xiang Wang, University of Science and Technology of China and affiliated with Institute of Artificial Intelligence, Institute of Dataspace, Hefei Comprehensive National Science Center; (6) Tat-Seng Chua, National University of Singapore. Table of Links Abstract & Introduction Methodology Experiments Related Work Conclusion and Future Work, Acknowledgment and References 5 CONCLUSION AND FUTURE WORK In this work, we systematically study the problem of bundle construction and define a more comprehensive formulation by considering all the three types of data, i.e., multimodal features, item-level user feedback data, and existing bundles. Based on this formulation, we highlight two challenges: 1) how to learn expressive bundle representations given multiple features; and 2) how to counter the modality missing, noise, and sparity problem. To tackle these challenges, we propose a novel method of Contrastive Learningenhanced Hierarchical Encoder (CLHE) for bundle construction. Our method beats a list of leading methods on four datasets of two application domains. Extensive ablation and model studies justify the effectiveness of the key modules. Despite the great performance that has been achieved by this work, there is still large space to be explored for bundle construction. First, the current evaluation setting is a little bit rigid and inflexible, it is interesting to extend it to more flexible setting to align with real applications. For example, given arbitrary number of seed items, the model is asked to construct the bundle. 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