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Hypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that focuses on extracting hypergraph features to improve multimodal representation. Our method utilizes a dual embedding strategy and leverages SHL to improve the accuracy and robustness of the model. To achieve this, we employ a hypergraph learning framework to extract global context effectively by capturing rich inter-modal dependencies. Additionally, we introduce a novel self-supervised learning (SSL) component that utilizes the interaction graph data, thereby strengthening the robustness of the model. By jointly optimizing hypergraph feature extraction and SSL, SHL significantly improves the performance of multimodal representation tasks. To validate the effectiveness of our approach, we construct two comprehensive multimodal micro-video recommendation datasets using publicly available data (TikTok and MovieLens-10M). Prior to dataset creation, we meticulously handle invalid entries and outliers and complete missing mode information using external auxiliary sources, such as YouTube. These datasets are made publicly available to the research community for evaluation purposes. Experimental results on the above recommendation datasets demonstrate that the proposed SHL approach outperforms state-of-the-art baselines, highlighting its superior performance in multimodal representation tasks.

Extreme multi-label text classification (XMTC) is an emerging and essential task in natural language processing. Its objective is to retrieve the most relevant labels for a text from a large set of labels while balancing time and accuracy. Although large-scale pre-trained models have brought new perspectives to this task, more attention should be given to valuable fine-tuned methods and the significant semantic gap between texts and labels. In this paper, we propose a novel guide network (GUDN) with a label reinforcement strategy based on label semantics to help fine-tune pre-trained models for classification. Experimental results demonstrate that GUDN outperforms state-of-the-art methods on Eurlex-4k and achieves competitive results on other popular datasets. In addition, we find that meaningless tokens can harm the Transformer-based model’s classification accuracy in another experiment. We conclude that GUDN is effective in the presence of solid semantics. Our source code is available at https://t.hk.uy/aFSH.