In recent years, Intelligent Personal Assistants (IPAs) have emerged as important tools in human–computer interaction, with a wide range of applications such as voice assistant, virtual customer service, and navigation. Capturing and understanding the prominent emotional needs of users is important for improving the quality of service of IPAs. Multimodal emotion recognition in conversation (MMERC) aimed at automatically identifying and tracking the emotional states of speakers during the dialogue process has become a crucial component for building emotional IPAs and attracted increasing attention. Current research in this field is based on graph simulation for cross-modal and single-modal interactions. However, these methods ignore the highly imbalanced class problem inherent in MMERC, leading to a decrease in the generalization ability of the model and an inability to effectively recognize minority emotion classes. Data mining methods use oversampling to solve the imbalanced classification, but they are unsuitable for MMERC as they disrupt the conversational coherence and modality alignment characteristics of multimodal emotion recognition datasets. To overcome these problems, this paper proposes an IMBA-MMERC, which is an effective framework to address the pervasive issue of class imbalance in MMERC. Within this framework, sample generation for multimodal conversation tackles the application challenges that exist in multimodal conversational emotion recognition datasets, and well-classified encouraging loss mitigates the performance degradation of the model on certain majority classes due to decision boundary deviations. On two English benchmark datasets and one Chinese public dataset, we used two performance indicators to demonstrate the effectiveness and superiority of the proposed IMBA-MMERC. Ablation experiment, case study, and histograms visualization further verify the well performance of the proposed framework. 

Generating and encouraging: An effective framework for solving class imbalance in multimodal emotion recognition conversation

This paper presents a comprehensive literature review of the research and application of machine learning (ML) algorithms in recommender systems (RS). The study aims to identify recent trends, explore real-life applications, and guide researchers in positioning their research activities in this domain published in 2023 (Jan-June). The findings are categorized into different domains including education, healthcare, ML algorithms (auto-encoders and reinforcement learning), e-commerce, and digital journalism. The review highlights the enhanced recommendation accuracy, increased scalability, personalization and context awareness, diverse ML techniques, and strategies for handling cold start and data sparsity, and the foundation for future advancements in ML algorithms for RSs considering the application in manufacturing enterprises. 

Unveiling the Latest Trends and Advancements in Machine Learning Algorithms for Recommender Systems: A Literature Review

With the rapid advancement of technology and the increasing demand for user-centric products, the integration of machine learning techniques has become imperative. This paper explores the transformative potential of integrating Particle Swarm Optimization (PSO), Deep Reinforcement Learning (DRL), and other machine learning algorithms such as neural networks, decision trees, and support vector machines into product design processes. Our novel hybrid framework leverages PSO’s global search capabilities and DRL’s adaptive learning to optimize product designs in a manner that traditional methods cannot achieve. By employing predictive modeling, clustering, and recommendation systems, designers can gain valuable insights into user needs and preferences, facilitating the creation of more intuitive and personalized products. We demonstrate that this integrated approach significantly improves design efficiency and user satisfaction. Key findings include a 25% reduction in design iteration time and a 30% increase in user satisfaction scores compared to traditional optimization methods. Additionally, our methodology provides a flexible and scalable solution adaptable to various product design contexts, showcasing its broad applicability and effectiveness. The incorporation of real-time feedback mechanisms allows for continuous refinement and adaptation of product designs to meet evolving user expectations. This study contributes to the field by presenting a comprehensive, multi-technique optimization framework that bridges existing gaps and sets a new standard for user-centric product design optimization. Ultimately, this research underscores the significance of embracing machine learning as a powerful tool for revolutionizing the product design landscape and delivering superior user experiences.

Machine Learning Algorithms for Improved Product Design User Experience

User personalized recommendation is increasingly vital in many industrial applications. How to precisely mine user's dynamic interests from multiple scenarios is a challenge task in Click-Through Rate (CTR) prediction. Existing works obtain representations based on feature engineering from training data. However, the CTR models are often sensitive to different users and scenarios. To address these concerns, we propose a novel approach, Multi-User-Multi-Scenario-Multi-Mode (MUMSMM), which learns user personalized preference from history behavior. Specifically, we adapt the cognitive gate-net, which can screen applicable networks automatically for different users and scenarios. Then, we design a feature mechanism to recognize valuable information, while also having an adaptive gate to optimize the hyper-parameters among tasks. Besides, we propose a novel interest unit, which can capture the user diverse interests and adjust the intensity. Furthermore, to generate a more stable interest representation, we employ transformer network. Our innovative approach can be flexibly embedded into the mainstream recommended CTR model. Extensive experiments on three public benchmark datasets (Alibaba, Amazon, and JD) show that the proposed MUMSMM model outperforms state-of- the-art models by an average improvement of +2.49% in AUC (Area Under Curve) and +4.33% of HR@10 (Hits Rate), respectively. 

A Multi-User-Multi-Scenario-Multi-Mode aware network for personalized recommender systems

Abstract In recent years, graph neural networks (GNNs) have showcased a strong ability to learn graph representations and have been widely used in various practical applications. However, many currently proposed GNN‐based representation learning methods do not retain neighbor‐based node similarity well, and this structural information is crucial in many cases. To address this issue, drawing inspiration from generative adversarial networks (GANs), we propose PNS‐AGNN (i.e., Preserving Node Similarity Adversarial Graph Neural Networks), a novel framework for acquiring graph representations, which can preserve neighbor‐based node similarity of the original graph and efficiently extract the nonlinear structural features of the graph. Specifically, we propose a new positive sample allocation strategy based on a node similarity index, where the generator can generate vector representations that satisfy node similarity through adversarial training. In addition, we also adopt an improved GNN as the discriminator, which utilizes the original graph structure for recursive neighborhood aggregation to maintain the local structure and feature information of nodes, thereby enhancing the graph representation's ability. Finally, we experimentally demonstrate that PNS‐AGNN significantly improves various tasks, including link prediction, node classification, and visualization. 

Preserving node similarity adversarial learning graph representation with graph neural network

Graph convolutional network combining node similarity association and layer attention for personalized recommendation

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Graph convolutional network combining node similarity association and layer attention for personalized recommendation