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Found 35 papers, 22 papers with code
Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation
To settle the above challenges, we propose a novel method HEKP4NBR, which transforms the knowledge graph (KG) into prompts, namely Knowledge Tree Prompt (KTP), to help PLM encode the OOV item IDs in the user's basket sequence.
Knowledge Prompt-tuning for Sequential Recommendation
Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge.
HomoGCL: Rethinking Homophily in Graph Contrastive Learning
Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting" learning scheme.
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification
Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios.
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering
The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets.
Low-Rank Tensor Based Proximity Learning for Multi-View Clustering
To deal with these problems, we propose a novel Low-rank Tensor Based Proximity Learning (LTBPL) approach for multi-view clustering, where multiple low-rank probability affinity matrices and consensus indicator graph reflecting the final performances are jointly studied in a unified framework.
Heterogeneous Tri-stream Clustering Network
Contrastive deep clustering has recently gained significant attention with its ability of joint contrastive learning and clustering via deep neural networks.
Deep Temporal Contrastive Clustering
Recently the deep learning has shown its advantage in representation learning and clustering for time series data.
Dual Information Enhanced Multi-view Attributed Graph Clustering
Specifically, the proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables GCN to capture the more essential low-level representations.
Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning
By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size.
Adaptively-weighted Integral Space for Fast Multiview Clustering
In view of this, we propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity.
Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks
Deep clustering has shown its promising capability in joint representation learning and clustering via deep neural networks.
Vision Transformer for Contrastive Clustering
Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation learning.
Gray Learning from Non-IID Data with Out-of-distribution Samples
We observe that both in- and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels.
Strongly Augmented Contrastive Clustering
In this paper, we present an end-to-end deep clustering approach termed Strongly Augmented Contrastive Clustering (SACC), which extends the conventional two-augmentation-view paradigm to multiple views and jointly leverages strong and weak augmentations for strengthened deep clustering.
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks
In view of this, this paper presents a Deep Clustering via Ensembles (DeepCluE) approach, which bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks.
Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users. However, the DNNs-based models usually suffer from high computational complexity, i. e., consuming very long training time and storing huge amount of trainable parameters.
Joint Multi-view Unsupervised Feature Selection and Graph Learning
Second, they often learn the similarity structure by either global structure learning or local structure learning, which lack the capability of graph learning with both global and local structural awareness.
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity
Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion.
Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering
Second, many of them overlook the local structures of multiple views and cannot jointly leverage multiple local structures to enhance the subspace representation learning.
G$^3$SR: Global Graph Guided Session-based Recommendation
G$^3$SR decomposes the session-based recommendation workflow into two steps.
PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks
However, recommender systems suffer from interaction data sparsity and data noise problems in reality.
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
Representation learning tries to learn a common low dimensional space for the representations of users and items.
Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency
To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and multi-view inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned.
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection
Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed.
Ranked #1 on
Community Detection
on Cora
Social and Information Networks Physics and Society 97R40
Ultra-Scalable Spectral Clustering and Ensemble Clustering
Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms.
Ranked #3 on
Image/Document Clustering
on pendigits
DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System
To solve this problem, many methods have been studied, which can be generally categorized into two types, i. e., representation learning-based CF methods and matching function learning-based CF methods.
Generative Dual Adversarial Network for Generalized Zero-shot Learning
Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem.
Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities
Upon the constructed graph, a transition probability matrix is defined, based on which the random walk process is conducted to propagate the graph structural information.
dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction
Mood disorders are common and associated with significant morbidity and mortality.
Human-Computer Interaction Computers and Society
Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond
The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research.
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick.
Robust Ensemble Clustering Using Probability Trajectories
To address these two limitations, in this paper, we propose a novel ensemble clustering approach based on sparse graph representation and probability trajectory analysis.
Locally Weighted Ensemble Clustering
Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering.
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
We present the normalized crowd agreement index (NCAI) to evaluate the quality of base clusterings in an unsupervised manner and thus weight the base clusterings in accordance with their clustering validity.
