Multimodal recommendation systems increasingly adopt GNNs to perform message passing on the user–item interaction graph and content-based item graphs to learn user and item representations. However, existing methods that use static-graph GNNs in multimodal recommendation face two key limitations: (i) representation homogenization, where repeated aggregation on a fixed topology makes node embeddings overly similar and weakens personalized signals; and (ii) incomplete context modeling, where similarity-based item graphs mainly capture local pairwise resemblance but miss group- or scenario-level relations that are far apart in the similarity space. Together, these limitations reduce the quality of learned node representations on static graphs and lead to suboptimal recommendations. To alleviate these limitations, we propose ESG-Rec, a unified multi-view framework that constructs three complementary structural views: degree-aware structural diversification to preserve individualized collaborative signals, content-based semantic modeling to provide stable local relations, and group-level semantic modeling to capture higher-order contextual information. ESG-Rec further performs multi-view contrastive alignment to learn consistent representations across views. Extensive experiments on benchmark datasets demonstrate that ESG-Rec consistently improves recommendation performance over strong multimodal baselines and effectively mitigates the above two limitations.

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model’s "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model’s ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method.

Multimodal Sentiment Analysis(MSA) mostly based on the assumption that all modalities are available. However, this assumption does not always hold in practice, and the performance of most multimodal sentiment analysis models can suffer significant degradation when some modalities are missing. To this end, we propose an Information Aggregation and Sentiment Enhance network(IASE) to handle missing modalities. Specifically, IASE models multimodal data as a bipartite graph structure that reduces the impact of missing modalities by aggregating complementary information to update out the representation of missing modalities. Aggregating samples of different sentiments leads to a decrease of sentiment intensity. The sentiment enhancement module is designed to enhance the sentiment intensity. Extensive experiments on several datasets show significant improvements of our method compared to several baselines.

Communities usually exhibit similar opinions, similar functions, or similar purposes, and recent years have witnessed the the resurgence of community detection in various fields. The node attribute network has gradually become the mainstream of the community network, however, most of existing community detection methods are limited by the high-dimensional node attribute and network topology, leading to the suboptimal performance. Inspired by recent deep learning-based community detection methods, we focus on building an unsupervised deep learning architecture to handle high-dimensional data in complex networks for community detection. To this end, we propose a novel Community Detection method based Deep Clustering and Graph Convolution auto-encoder Network (CD-DCGCN). Our CD-DCGCN designs an end-to-end framework consisting of dual auto-operations, one is a graph convolution auto-encoder, the other is community auto-detection. In addition, we realize the cooperative work of the dual auto-operations by constructing a joint optimized function. Our experimental results on nine attribute network benchmark datasets show that the proposed CD-DCGCN can obtain promising performance compared with several popular baseline methods.

A significant goal in an open-domain dialogue system is to make chatbots generate more persona coherent responses given a context. To achieve this goal, some researchers attempt to introduce persona information into neural dialogue models. However, these neural dialogue models describe excessively persona traits during the conversation, which still suffer from the problem of generating boring and meaningful responses. In this paper, we divide the open-domain personalized dialogue generation task into two processes, persona recognition and persona fusion. In the persona recognition process, we use the pre-training model to encode the personas and conversation history independently, which is beneficial to the persona information fusion. Then, we design a dynamic persona fusion mechanism to effectively mine the relevance of dialogue context and persona information, and dynamically predict whether to incorporate persona features in the process of the dialogues. Our model outperforms with 1.23% in Acc., 0.36% in BLEU, 0.92% in F1, and 0.036% in Distinct than baseline models. The experimental results on the ConvAI2 dataset illustrate that the proposed model is superior to baseline approaches for generating more coherent and persona consistent responses.

Deep clustering aims to cluster unlabeled real-world samples by mining deep feature representation. Most of existing methods remain challenging when handling high -dimensional data and simultaneously exploring the complementarity of deep feature representation and clustering. In this paper, we propose a novel Deep Variational Attention Encoder-decoder for Clustering (DVAEC). Our DVAEC improves the representation learning ability by fusing variational attention. Specifically, we design a feature-aware automatic clustering module to mitigate the unreliability of similarity calculation and guide network learning. Besides, to further boost the performance of deep clustering from a global perspective, we define a joint optimization objective to promote feature representation learning and automatic clustering synergistically. Extensive experimental results show the promising performance achieved by our DVAEC on six datasets comparing with several popular baseline clustering methods.

With the rapid growth of user scale, it is so meaningful to explore the users who carry more valuable information in the user group. The importance of the individual users in the recommender system can improve the recommendation efficiency of the recommender system, enhance the robustness of the recommender system, but there is little research work in this path and can not determine a more effective method. To solve this problem, we propose a method based on multi-label clustering to determine core user, and define the concept of correlation between user and label cluster, user location weight, considered the potential relationship between users and labels. At the same time, we proposed a new method based on multi-label clustering and core user, according to experiments demonstrate the effectiveness of the proposed algorithm, we have a more significant upgrade in the recommendation accuracy and diversity.