Multimodal Sentiment Analysis (MSA) aims to comprehensively understand human affective states. To achieve this goal, it integrates heterogeneous modalities, including text, audio, and visual information. However, semantic misalignment within multimodal data and the insufficiency of multimodal feature fusion pose challenges to achieving accurate sentiment prediction. To this end, we propose a Cross-modal Joint Semantic Transformer (CJST) model to achieve cross-modal semantic alignment, thereby enhancing sentiment prediction accuracy. First, we design a Singular Value Decomposition (SVD) based cross-modal semantic alignment strategy that can decouple the time and semantic components of unimodal inputs to reduce the impact of misalignment noise and temporal redundancy. Then, a feature-level low-rank multimodal fusion strategy is developed to achieve high-order interactions among semantic features through tensor-based fusion within the low-rank space. Finally, we conduct various experiments on two well-known MSA benchmark datasets. Extensive experimental results indicate that the proposed CJST model outperforms or matches the state-of-the-art methods.

In the context of big data enabling e-commerce, content platforms, and social networks, Recommendation Systems (RSs) play a crucial role in providing personalized items and services suggestions to users. Among the various RSs approaches, Collaborative Filtering (CF) approaches, particularly Singular Value Decomposition++ (SVD++) algorithm, have gained widespread adoption due to their ability to leverage both explicit and implicit feedback derived from user history interaction data. This data includes user-item status, purchase history, and user interaction. However, traditional matrix-based methods, such as SVD++, often struggle to capture the multi-dimensional features and temporal dynamics inherent in real-world user-item data. To address this limitation, we propose a novel tensor-based High-Order SVD++ (HO-SVD++) recommendation method. This approach employs tensors to model multi-feature interaction data, with a particular emphasis on temporal dynamics. Specifically, we introduce a novel method for segmenting recommendation data based on user rating periods and construct a tensor to encapsulate these temporal features. Additionally, we propose a user-item correlation CF method that extracts implicit feature relationships between users and items. Building on this, we present the HO-SVD++ method, which is specifically designed for recommendation tasks involving multiple latent factors. Furthermore, we introduce a comprehensive recommendation framework based on the HO-SVD++ method. Experimental results on three classical recommendation datasets demonstrate that the proposed HO-SVD++ algorithm outperforms several classical and neural recommendation baselines in terms of recommendation accuracy.