Data Sparsity Issue Research Articles

A Unified Graph Theory Approach: Clustering and Learning in Criminal Data

Crime report clustering plays a critical role in modern law enforcement, enabling the identification of patterns and trends essential for proactive policing. However, traditional clustering approaches face significant challenges with the complex, unstructured nature of crime reports and their inherent sparse relationships. While graph-based clustering shows promise, issues of noise sensitivity and data sparsity persist. This study introduces a unified approach integrating spectral graph-based clustering with Graph Convolutional Networks (GCN) to address these challenges. The proposed approach encompasses data collection, preprocessing, linguistic feature extraction, vectorization, graph construction, graph learning, and clustering to effectively capture the intricate similarities between crime reports. The proposed approach achieved significant improvements over existing methods: a Silhouette Score of 0.77, a Davies–Bouldin Index of 0.51, and consistent performance across varying dataset sizes (100–1000 nodes). These results demonstrate the potential for enhanced crime pattern detection in law enforcement operations.

Rapid groundwater assessments in gneissic settings using transient electromagnetics – examples from Togo

Towed transient electromagnetics (tTEM) is applied in the Plateau Region of Togo for rapid and high-resolution characterization of groundwater systems surrounding six villages. Past drilling efforts in the region often resulted in unsuccessful or low-yielding boreholes, due in large part to sparse hydrogeological data at the scale relevant for borehole siting surrounding each village. Towed TEM systems are shown to be well-suited to addressing this data sparsity issue, enabling the mapping of large regions surrounding each village in relatively short field times (two days per village in this work). Comparison with borehole lithology logs reveals that a towed electromagnetic system can accurately characterize the thickness of the weathered zone in gneissic settings, as well as to delineate elevated saturation fractured zones within the gneissic bedrock in several cases. Boreholes drilled based on tTEM surveys were able to intercept fresh groundwater resources in each of the investigated villages.

Role of AI in reducing global Plastics use: Predictive analytics for global sustainability

The growth, from a global perspective, in plastic waste sees its necessary promise in Artificial Intelligence. The driving force of Machine Learning, Computer Vision, and Predictive Analytics, shapes each style of waste management and effective collection routes to recycling. In this paper, therefore, the discussion will be on how AI can ensure a reduction in plastic waste, focusing on developing countries such as Nigeria. The theoretical underpinning of this research is on AI adoption, the Technology Acceptance Model, a few real-world case studies in AI for waste reduction, and many challenges that need to be focused on due to issues of data sparsity, infrastructure limitations, and ethics. Using these challenges for unlocking the full potential of AI in the direction of a more sustainable future, with minimal plastic waste at the forefront of environmental well-being, could be better negotiated. As the global community faces the pressing need to tackle plastic pollution, especially in areas with inadequate waste management systems and severe environmental challenges AI technologies present groundbreaking solutions to improve waste management methods, eliminate environmental harm, and foster sustainable growth. A crucial factor influencing the future of AI-driven plastic waste reduction in developing countries is the ongoing progress and implementation of AI technologies.

LLMasMMKG: LLM Assisted Synthetic Multi-Modal Knowledge Graph Creation For Smart City Cognitive Digital Twins

The concept of a Smart City (SC) Cognitive Digital Twin (CDT) presents significant potential for optimizing urban environments through sophisticated simulations, predictions, and informed decision-making. Comprehensive Knowledge Representations (KRs) that effectively integrate the diverse data streams generated by a city are crucial to realizing this potential. This paper addresses this by introducing a novel approach that leverages Large Language Models (LLMs) to automate the construction of synthetic Multi-Modal (MM) Knowledge Graphs (KGs) specifically designed for a SC CDT. Recognizing the challenges in fusing and aligning information from disparate sources, our method harnesses the power of LLMs for natural language understanding, entity recognition, and relationship extraction to seamlessly integrate data from sensor networks, social media feeds, official reports, and other relevant sources. Furthermore, we explore the use of LLM-driven synthetic data generation to address data sparsity issues, leading to more comprehensive and robust KGs. Initial outputs demonstrate the effectiveness of our approach in constructing semantically rich and interconnected synthetic KGs, highlighting the significant potential of LLMs for advancing SC CDT technology.

Variational Type Graph Autoencoder For Denoising On Event Recommendation

Recommendations for events play a pivotal role in facilitating the discovery of upcoming intriguing events within Event-Based Social Networks (EBSNs). Previous research has established the crucial significance of mining contextual features and implicit relationships to enhance recommendation performance and alleviate data sparsity issues. However, the noise inherent in contextual features exacerbates data sparsity and hampers the ability of previous methods to explore implicit relationships for mitigating data sparsity. To address this challenge, we propose a variational type graph autoencoder model that attenuates the influence of noise in different types of context features by introducing type-specific latent variables. Firstly, we introduce a heterogeneous denoising convolution module composed of two components: 1) Denoising attention aggregation is proposed to mitigate the influence of noisy structures and uncover implicit relationships. 2) A heterogeneous normalization module leverages context features within the same type to alleviate the effects of noise in context features and data sparsity. Furthermore, we propose a learnable heterogeneous mixture prior that assists in assigning different priors to distinct types of latent variables, effectively modeling different types of contextual features. Through comprehensive experiments conducted on real-world datasets, we demonstrate the compelling performance of our model compared to state-of-the-art competitive approaches.

Predicting trajectory destinations based on diffusion model integrating spatiotemporal features and urban contexts

ABSTRACT Accurately predicting travel destinations in a city from partial trajectories is crucial for many location-based services. This task faces two challenges: data sparsity and the complex probability distribution of destinations. Previous studies neglected the fusion of spatiotemporal features and urban contexts, which limited their effectiveness in characterizing trajectory patterns and exacerbated data sparsity issues. Most methods either predicted a single destination or the probability of each predefined sub-region, but they struggled with the modifiable areal unit problem. To address these problems, we propose an approach based on the diffusion probabilistic model, comprising a trajectory encoder and a diffusion decoder. The trajectory encoder extracts four embeddings to represent spatiotemporal features and urban contexts, integrating them using an attention mechanism to mitigate data sparsity. The diffusion decoder recovers the complex destination probability distribution and predicts multiple candidate destinations without predefined regional units, effectively addressing the modifiable areal unit problem. Experiments on driving trajectories in Shenzhen, China, show an average improvement of 10.80% across four metrics compared to state-of-the-art methods. We also observed the influence of trajectory length and origin location on prediction accuracy. The results validate the importance of modeling destination probability distributions, as well as integrating spatiotemporal features and urban contexts.

Enhanced knowledge graph recommendation algorithm based on multi-level contrastive learning

Integrating the Knowledge Graphs (KGs) into recommendation systems enhances personalization and accuracy. However, the long-tail distribution of knowledge graphs often leads to data sparsity, which limits the effectiveness in practical applications. To address this challenge, this study proposes a knowledge-aware recommendation algorithm framework that incorporates multi-level contrastive learning. This framework enhances the Collaborative Knowledge Graph (CKG) through a random edge dropout method, which constructs feature representations at three levels: user-user interactions, item-item interactions and user-item interactions. A dynamic attention mechanism is employed in the Graph Attention Networks (GAT) for modeling the KG. Combined with the nonlinear transformation and Momentum Contrast (Moco) strategy for contrastive learning, it can effectively extract high-quality feature information. Additionally, multi-level contrastive learning, as an auxiliary self-supervised task, is jointly trained with the primary supervised task, which further enhances recommendation performance. Experimental results on the MovieLens and Amazon-books datasets demonstrate that this framework effectively improves the performance of knowledge graph-based recommendations, addresses the issue of data sparsity, and outperforms other baseline models across multiple evaluation metrics.

Contrastive Clustering Learning for Multi-Behavior Recommendation

Increasing multiple behavior recommendation models have achieved great successes. However, many models do not consider commonalities and differences between behaviors and data sparsity of the target behavior. This paper proposes a novel Multi-behavior Recommendation Model (MBRCC) based on Contrastive Clustering Learning. Specifically, the graph convolutional network (GCN) is employed to obtain the embeddings of users and items, respectively. Then, three kinds of tasks (including behavior-level embedding, instance-level embedding and cluster-level embedding) are designed to optimize the embeddings of users and items. In behavior-level embedding, we design an adaptive parameter learning strategy to analyze the impact of auxiliary behaviors on the target behavior. Then, the embeddings of users for each behavior are weighted to obtain the final embeddings of users. In instance-level embedding, we employ contrastive learning to analyze the instances of user and item for mitigating the issue of data sparsity. In cluster-level embedding, we design a new cluster contrastive learning method to capture the similarity between groups of user and item. Finally, we combine these three tasks to improve the quality of the embeddings of users and items. We conduct extensive experiments on three real-world datasets and experimental results indicate that the MBRCC remarkably outperforms numerous existing recommendation models.

Multi-view graph contrastive learning for social recommendation.

With the rapid popularity of online social media, recommendation systems have increasingly harnessed social relations to enhance user-item interactions and mitigate the data sparsity issue. Beyond social connections, the semantic relatedness among items has emerged as a crucial factor in comprehending their inherent connections. In this work, we propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social networks and item knowledge graphs into modeling users preferences within recommendation systems. To facilitate the alignment of different views, we introduce a dedicated multi-view contrastive learning process that extracts rich information from each view and foster mutual enhancement. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our framework over representative recommendation methods. Furthermore, ablation studies offer a deeper understanding of the mechanisms underlying our framework.

Interaction-knowledge semantic alignment for recommendation

In order to alleviate the issue of data sparsity, knowledge graphs are introduced into recommender systems because they contain diverse information about items. The existing knowledge graph enhanced recommender systems utilize both user–item interaction data and knowledge graph, but those methods ignore the semantic difference between interaction data and knowledge graph. On the other hand, for the item representations obtained from two kinds of graph structure data respectively, the existing methods of fusing representations only consider the item representations themselves, without considering the personalized preference of users. In order to overcome the limitations mentioned above, we present a recommendation method named Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR). By introducing a semantic alignment module, the semantic difference between the interaction bipartite graph and the knowledge graph is reduced. The representation of user is integrated during the fusion of representations of item, which improves the quality of the fused representation of item. To validate the efficacy of the proposed approach, we perform comprehensive experiments on three datasets. The experimental results demonstrate that the IKSAR is superior to the existing methods, showcasing notable improvement.

Towards a word similarity gold standard for Akkadian: creation and model optimization

Abstract We present a word similarity gold standard for Akkadian, a language documented in ancient Mesopotamian sources from the 24th century BCE until the first century CE. The gold standard comprises 300 word pairs ranked by their paradigmatic similarity by five independently working Assyriologists. We use the gold standard to tune PMI + SVD and fastText models to improve their performance. We also present a hyper-parametrized PMI + SVD model for building count-based word embeddings, that aims to deal with the data sparsity and repetition issues encountered in Akkadian texts. Our model combines Dirichlet smoothing with context distribution smoothing, and uses context similarity weighting to down-sample distortion caused by formulaic litanies and partially or fully duplicated passages.

ANAGL: A Noise-Resistant and Anti-Sparse Graph Learning for Micro-Video Recommendation

In recent years, graph convolutional networks (GCNs) have seen widespread utilization within micro-video recommendation systems, facilitating the understanding of user preferences through interactions with micro-videos. Despite the commendable performance exhibited by GCN-based methodologies, several persistent issues demand further scrutiny. Primarily, most user-micro-video interactions involve implicit behaviors, such as clicks or abstentions, which may inadvertently capture irrelevant micro-video content, thereby introducing significant noise (false touches, low watch-ratio, low ratings) into users’ histories. Consequently, this noise undermines the efficacy of micro-video recommendations. Moreover, the abundance of micro-videos has resulted in fewer interactions between users and micro-video content. To tackle these challenges, we propose a noise-resistant and anti-sparse graph learning framework for micro-video recommendation. Initially, we construct a denoiser that leverages implicit multi-attribute information (e.g., watch-ratio, timestamp, ratings, and so on) to filter noisy data from user interaction histories. This process yields high-fidelity micro-video information, enabling a more precise modeling of users’ feature preferences. Subsequently, we employ a multi-view reconstruction approach and utilize cross-view self-supervised learning to gain insights into user and micro-video features. This strategic approach effectively mitigates the issue of data sparsity. Extensive experiments conducted on two publicly available micro-video recommendation datasets validate the effectiveness of our proposed method. For in-depth details and access to the code, please refer to our repository at “ https://github.com/kbk12/ANAGL.git .”

A personality-guided preference aggregator for ephemeral group recommendation

Ephemeral group recommendation (EGR) aims to suggest items for a group of users who come together for the first time. Existing work typically consider individual preferences as the sole factor in aggregating group preferences. However, they neglect to take into account the importance of the individual inherent factors, such as personality, and thus fail to accurately simulate the group decision-making process. Additionally, these methods often struggle due to insufficient interactive records. To tackle these issues, a Personality-Guided Preference Aggregator (PEGA) is proposed, which guides the preference aggregation of group members based on their personalities, rather than relying solely on their preferences. Specifically, implicit personalities are first extracted from user reviews. Hyper-rectangles are then used to aggregate individual personalities to obtain the “Group Personality”, which allows for the learning of personality distributions within the group. Subsequently, a personality attention mechanism is employed to aggregate group preferences, and a preference-based fine-tuning module is used to balance the weights of personality and preferences. The role of personality in this approach is twofold: (1) To estimate the importance of individual users in a group and provide explainability; (2) To alleviate the data sparsity issue encountered in ephemeral groups. Experimental results demonstrate that, on four real-world datasets, the PEGA model significantly outperforms related baseline models in terms of classification accuracy and interpretability. Moreover, empirical evidence supports the idea that personality plays a pivotal role in enhancing the performance of EGR tasks.

Cascading graph contrastive learning for multi-behavior recommendation

Traditional recommendation techniques often prioritize target behavior in practical recommendation scenarios(e.g., follow, play and buy). However, these approaches suffer from data sparsity issues and may not fully capture user’s personal preferences. To address this deficiency, multi-behavior recommendation technology has emerged, leveraging users’ multi-behavioral interactions for recommendation. Nevertheless, certain multi-behavior recommendation methods learning behavioral information from each behavior separately and then aggregate them before making recommendation, which inadvertently neglects the intrinsic connections between different behaviors. In some scenarios, user behavior often occurs in a fixed order, such as view -> cart -> buy in e-commerce platforms. In this work, we propose a novel Cascading Graph Constrastive Learning (CGCL) framework for Multi-Behavior recommendation. Specifically, we devise a graph contrastive learning block to learn distinctive user behavioral representations for each type of interaction. Leveraging the recommendation task, we aim to capture user preferences, while the contrastive learning provides supplementary supervisory signals to refine the user and item representation. By acknowledging the sequential order of behaviors, we utilize the cascading structure within our model to iteratively propagate and purify the personalized preferences of users. Extensive experimental results and ablation studies on three real-world datasets have shown that our CGCL framework outperforms various state-of-the-art recommendation methods and validated the effectiveness of our approach.

Graphical contrastive learning for multi-interest sequential recommendation

Multi-interest sequential recommendation aims to deliver accurate recommendations by modeling users’ sequential behaviors and multi-faceted interests. Contrastive learning (CL) possesses an inborn advantage in distinguishing users’ multiple interests. However, existing CL-based multi-interest sequential recommenders tend to overlook the issue of data sparsity which deteriorates model performance. Graph neural network (GNN), on the other hand, has been proven as an effective measure of tackling the data sparsity problem. However, incorporating GNN into CL requires a delicate design to simultaneously achieve distinctive interest representation learning and relieve data sparsity. To address the challenges, we propose a Graphical Contrastive Learning for Multi-Interest Sequential Recommendation (GCL4MI), which first extracts user interest and constructs interest-specific graph based on item clusters and then perform graphical contrastive learning to enhance the independence between multiple interests. Furthermore, we explore different extensible CL sampling strategies with the facilitation of GNN to learn distinct interests in a high-cohesion and low-coupling manner. Extensive experiments on three real-world datasets demonstrate that our proposed GCL4MI significantly outperforms state-of-the-arts, with average lifts of 7.0%, 7.1%, and 5.8% on recall, NDCG, and HR, respectively.

Elevating recommender systems: Cutting-edge transfer learning and embedding solutions

In today’s information age and connected economy, Recommender Systems (RS) plays a vital role in managing information overload and delivering personalized suggestions to users. This paper introduces a multistage model that leverages multimodal data embedding and deep transfer learning to accurately capture user preferences and item characteristics, resulting in highly tailored recommendations. A key innovation in this model is the incorporation of an image dataset in the second phase, which addresses cold-start problems for new items by providing additional visual context. Our approach excels in overcoming challenges related to data sparsity and cold-start issues, thereby providing users with realistic and relevant product recommendations. To validate the effectiveness of the proposed model, we conducted extensive evaluations using three diverse datasets: data from Brazilian e-commerce platforms, the MovieLens 1M dataset, and the Amazon Product Review dataset. These evaluations involved comprehensive comparisons with standard RS methods to assess performance improvements. The results indicate that our proposed model significantly outperforms traditional RS techniques in terms of accuracy and reliability. Our model provides more accurate and meaningful recommendations by effectively addressing issues such as cold-start and data scarcity. Specifically, the model achieved Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) scores of 0.5883 and 0.4012, respectively, which demonstrate its superior performance metrics across all datasets tested.

Hybrid music recommendation with graph neural networks

Modern music streaming services rely on recommender systems to help users navigate within their large collections. Collaborative filtering (CF) methods, that leverage past user–item interactions, have been most successful, but have various limitations, like performing poorly among sparsely connected items. Conversely, content-based models circumvent the data-sparsity issue by recommending based on item content alone, but have seen limited success. Recently, graph-based machine learning approaches have shown, in other domains, to be able to address the aforementioned issues. Graph neural networks (GNN) in particular promise to learn from both the complex relationships within a user interaction graph, as well as content to generate hybrid recommendations. Here, we propose a music recommender system using a state-of-the-art GNN, PinSage, and evaluate it on a novel Spotify dataset against traditional CF, graph-based CF and content-based methods on a related song prediction task, venturing beyond accuracy in our evaluation. Our experiments show that (i) our approach is among the top performers and stands out as the most well rounded compared to baselines, (ii) graph-based CF methods outperform matrix-based CF approaches, suggesting that user interaction data may be better represented as a graph and (iii) in our evaluation, CF methods do not exhibit a performance drop in the long tail, where the hybrid approach does not offer an advantage.

Lightweight intrusion detection model based on CNN and knowledge distillation

The problem of network attacks is a primary focus in the domain of intrusion detection. Models face significant challenges in recognizing intrusion behaviors, particularly when dealing with high-dimensional and sparse datasets. Traditional machine learning methods often struggle with these dimensionality issues. In contrast, deep learning, a crucial technology in intrusion detection, excels at managing high-dimensional data. However, traditional image coding methods do not adequately address data sparsification and often overlook the spatial continuity among features. The Fourier transform is a promising solution for data sparsity issues, as it effectively mitigates the impact by converting data into a different domain. Inspired by the Fourier transform, this paper proposes a lightweight intrusion detection model called TFTKD, based on Convolutional Neural Networks (CNN) and knowledge distillation. The model applies a two-dimensional Fourier transform to convert grayscale images from the time domain to the frequency domain. This transformation enhances the similarity between neighboring pixels, effectively addressing data sparsification. During the training phase, a teacher network, comprising an 8-layer CNN, is pre-trained. In the distillation phase, a one-layer CNN serves as the student network, employing Self-adaptive Temperature Knowledge Distillation to enhance the student's generalization capabilities. This approach results in a compact student network model with a constrained parameter count, demonstrating superior learning efficiency and accuracy compared to state-of-the-art methods. Experimental validation was conducted using four publicly accessible intrusion detection datasets, demonstrating the effectiveness of the proposed method.

CF-MGAN: Collaborative filtering with metadata-aware generative adversarial networks for top-N recommendation

Generative Adversarial Networks (GANs) have several advantages over latent variable and autoregressive models, such as directly computing the input data distribution, fast sampling, and best samples generated. Furthermore, the metadata information of users and items plays a crucial role in effectively learning the user-item latent representations. Existing neural network approaches are deterministic, incapable of accounting for user-item latent representations, and the rating prediction process is calculative rather than generative. However, only some papers have utilized GANs for information retrieval tasks. However, they have several limitations, such as the inability to abstract data effectively, the limitation of the model's ability to learn accurate latent representations, and the absence of metadata information. To address these issues, we propose a GAN-based recommendation framework named Collaborative Filtering with Metadata-awareGenerative Adversarial Networks (CF-MGAN) to learn complex rating distribution for collaborative filtering (CF) tasks. We use a negative item generator for the implicit user purchase vector to prevent GAN from generating biased ratings. We exploited metadata information to overcome data sparsity issues. We use Variational Autoencoder to abstract user-item metadata features, a regularizer for GAN-generated rating. Extensive experiments on datasets reveal the effectiveness of the proposed model, showing an improvement in the top-N recommendation task.

Deep learning model for precise prediction and design of low-melting point phthalonitrile monomers

Phthalonitrile is a widely applied resin due to its outstanding high-temperature resistance and versatility. However, the high melting point of its monomer limits its range of applications. Due to the scarcity of data, traditional deep learning models face challenges in accurately predicting the properties of phthalonitrile monomers. In this work, we propose a novel deep learning model based on techniques such as molecular similarity weighting, pre-training parameter averaging, and hybrid neural networks to address the issue of data sparsity. Our model exhibits exceptionally high accuracy for predicting the melting point of phthalonitrile monomers, e.g., the mean absolute error on the experimental dataset is 15.3 °C. To explore novel low-melting monomers, we develop a high-throughput molecule generation software and use our model to filter the virtual molecules, and ultimately obtain candidates that meet low-melting and synthesizability requirements. Moreover, we have successfully synthesized one candidate molecule, and the high consistency between our predictions and experimental measurements again demonstrates our model’s accuracy. Meanwhile, molecule dynamic simulations are conducted to provide explanations for the reduction in melting point. This study not only provides guidance for designing phthalonitrile monomers, but also proposes an innovative approach for exploring of new materials under conditions of sparse data.