Despite advancements in visual question answering, challenges persist with documents like financial reports, often structured in complicated tabular structures with complex numerical computations. An alternative approach, the pipeline-driven methodology, includes table recognition (TR) and table question-answering (TQA). Recent advancements in TR support this approach with better accuracy and interpretability. However, real-world tables usually represent hierarchical tables. They pose additional challenges due to merged cells and indents, necessitating a specific approach for hierarchical relationship extraction. In this paper, we propose TRH2TQA (Table Recognition with Hierarchical Relationships to Table Question-Answering) for business table images. It consists of three modules on table images with question-answer pairs. First, the TR module extracts structure and textual content from table images into HTML format. Second, post-structure extraction is applied to identify header and hierarchical relationships using predicted column span and bounding box. Finally, this information is combined with natural language questions in the TQA module to generate the answer through the decoder. In extensive experiments, TRH2TQA outperforms in questionanswering performance on the VQAonBD 2023 dataset.

Cross-domain recommender systems (CDRSs) enhance recommendations by transferring knowledge of overlapping users across two domains. Deep canonical correlation analysis (DCCA) shows promising results in CDRSs by maximizing correlations between representations of overlapping users, enabling cross-domain knowledge transfer that depends on the degree of relationship between domains. As a result, DCCA selectively shares only relevant knowledge, alleviating the problem of noisy representation found in traditional CDRSs, where they transfer knowledge regardless of the correlation strength between domains. Although DCCA is used for user transfer, item transfer, referring to the transfer of explicit knowledge of the same items between domains, is impossible due to the absence of overlapping items to facilitate direct knowledge transfer. Meanwhile, graph neural networks (GNNs) embed users and items from separate user and item graphs in each domain. Therefore, better representations are obtained from captured complex relationships and collaborative signals. To construct graphs of overlapping items, latent linkages among items between domains could be discovered by the neural topic model (NTM), forming new graphs representing the latent relationships. Therefore, COLANet, a GNN-based CDRS, is proposed to solve the DCCA limitation on item transfer by proposing the extraction of item representations that do not exist in another domain using latent characteristics. First, user-user graphs are constructed using user similarity, and the item-topic graph is constructed using latent topics learned from item descriptions with NTM. Hence, user and item graphs of each domain are constructed separately, preventing domain relationship misalignment. Second, these graphs are fed to GNN to obtain user and item representations. Third, these representations are fed to DCCA to transfer knowledge between user-user and item-item. Finally, correlated user and item representations of each domain are used to predict ratings. The experiments demonstrate that COLANet outperforms the baselines across four pairs of domains, including both similar and different domains.