In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions.

Traffic flow prediction remains a critical issue in intelligent transport systems. Despite significant efforts in traffic flow modeling, existing approaches exhibit several notable limitations: (i) Most models fail to capture traffic flow similarities over long distances and extended periods; (ii) They struggle to account for spatio-temporal heterogeneity induced by varying traffic flow patterns; (iii) Due to their static modeling approach, they struggle to effectively capture the intricate spatio-temporal entanglement. To address these challenges, we propose a traffic flow prediction framework based on self-supervised learning spatio-temporal entanglement transformer(SSL-STMFormer). This framework adopts a self-supervised learning paradigm, leveraging a transformer architecture that captures richer spatio-temporal information to better represent traffic flow patterns. Specifically, a temporal attention module and a spatial attention module are employed to capture the spatio-temporal dependencies of traffic dynamics, respectively, and spatio-temporal entanglement-aware methods are introduced to allow the model to perceive spatio-temporal entanglement and thus better modelling of real traffic environments. Furthermore, to achieve adaptive spatio-temporal self-supervised learning, adaptive data augmentation is applied to the input traffic flow data, and the traffic flow prediction task is enhanced with temporal heterogeneity module and spatial heterogeneity module. Extensive experimental evaluations conducted on six publicly available real-world transportation datasets demonstrate that our method achieves substantial improvements across these datasets.

Emotion recognition in conversations (ERC) is a pivotal component of affective computing, involving a common two-stage paradigm where pre-trained language models first extract context-independent features, followed by the encoding of contextual information and the modeling of emotional dependencies. This paradigm faces two challenges: (1) Existing methods struggle to capture both the intra-dialogue emotional continuity and the inter-dialogue semantic similarity. (2) The complexity of emotional elicitation processes gives rise to entangled dependencies, termed “latent dependencies”, which are difficult for current methods to detect and analyze. To overcome these challenges, we propose a Hybrid-Context Encoder with an Automated Latent Dependency Mining model for ERC. Specifically, we examine the emotional continuity and the semantic similarity from the standpoint of context encoders. We experimentally find that context encoders with different architectures exhibit distinct benefits. Based on these findings, we design a hybrid contextual encoding module that effectively combines the strengths of various encoders. Additionally, we design a lightweight generative module for latent dependency mining that autonomously generates a context mask, enabling the effective discovery of latent dependencies. We conduct extensive experiments on three datasets in the text modality. Our model achieves the best performance, which validates the superiority of our approach.

Social recommendations leverage social networks to augment the performance of recommender systems. However, the critical task of denoising social information has not been thoroughly investigated in prior research. In this study, we introduce a hierarchical denoising robust social recommendation model to tackle noise at two levels: 1) intra-domain noise, resulting from user multi-faceted social trust relationships, and 2) inter-domain noise, stemming from the entanglement of the latent factors over heterogeneous relations (e.g., user-item interactions, user-user trust relationships). Specifically, our model advances a preference and social psychology-aware methodology for the fine-grained and multi-perspective estimation of tie strength within social networks. This serves as a precursor to an edge weight-guided edge pruning strategy that refines the model's diversity and robustness by dynamically filtering social ties. Additionally, we propose a user interest-aware cross-domain denoising gate, which not only filters noise during the knowledge transfer process but also captures the high-dimensional, nonlinear information prevalent in social domains. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our proposed model against state-of-the-art baselines. We perform empirical studies on synthetic datasets to validate the strong robustness of our proposed model.

Social media has emerged as a vital platform for communication, information sharing, and acquisition. Predictive analysis of social media data has wide applications, such as sentiment examination and social network analysis. However, existing work often directly utilizes social media data for training, neglecting the issue of mismatched text and images. This neglect can lead to confusion about the contents, thereby affecting the identification of trending topics and the accuracy of social media predictions. In this paper, an approach named Dual-Stream Pre-training Transformer (DSPT) is introduced to address this gap. In DSPT, we use a Visual-Language Model (VLM) and a Language Model (LM) to separately learn from image and text data, mitigating the impact of text-image mismatches. Moreover, to enhance the understanding of the model to social media data, we conduct incremental pre-training for both models. To achieve better feature interaction, we construct an integrated regression module combining LightGBM and CatBoost, jointly predicting the extracted feature embeddings. This dual-stream multimodal feature extraction method improves the performance of predictive tasks. Experimental results validate the effectiveness of our approach, demonstrating its potential and providing deeper insights into multimodal data mining in social media.

BACKGROUND: Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. RESULTS: We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets. CONCLUSIONS: This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.