Author name cluster

Lu Yu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

20 papers

2 author rows

AIIM Journal 2026 Journal Article

Multi-domain based heterogeneous network for drug-target interaction prediction

Changjian Zhou
Yutong Liu
Lu Yu
Zhenyuan Zhao
Jia Song
Wensheng Xiang

Recent studies have emphasized the importance of computational approaches in predicting drug-target interactions (DTIs) for drug discovery and chemogenomics studies. Precise prediction of DTIs plays a crucial role in exploring a vast space of drug compounds. However, existing in silico approaches suffer from the following limitations. Firstly, most molecular representation learning methods neglect the sub-structural characteristics of drug-target pairs (DTPs), resulting in challenging interpretations of the predictions. In addition, many models focus on limited in-domain datasets, exhibiting unsatisfactory results when applied to predict new DTIs. To mitigate these defects, we present MHNF-DTI here, a Multi-domain based Heterogeneous Network Framework designed for the integrated prediction and interpretation of DTIs with high interpretability and generalization ability. Importantly, the novel framework utilizes a transformer encoder that integrates multilayer graph attention networks, effectively capturing the sub-structural properties of drug compounds and target sequences, make it able to adapt to the shared structures of different DTPs while enhancing the molecular representation capabilities. Additionally, to improve the generalization ability of the model and mitigate the potential hidden ligand bias pitfalls, a new multi-domain label reversal dataset is constructed for training. Experimental results demonstrated that the proposed MHNF-DTI improved DTI prediction performance compared to the existing state-of-the-art baselines.

Details DOI

NeurIPS Conference 2025 Conference Paper

Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration

Yifeng Yu
Lu Yu

Score-based diffusion models have emerged as powerful tools in generative modeling, yet their theoretical foundations remain underexplored. In this work, we focus on the Wasserstein convergence analysis of score-based diffusion models. Specifically, we investigate the impact of various discretization schemes, including Euler discretization, exponential integrators, and midpoint randomization methods. Our analysis provides the first quantitative comparison of these discrete approximations, emphasizing their influence on convergence behavior. Furthermore, we explore scenarios where Hessian information is available and propose an accelerated sampler based on the local linearization method. We establish the first Wasserstein convergence analysis for such a Hessian-based method, showing that it achieves an improved convergence rate of order $\widetilde{\mathcal{O}}\left(\frac{\sqrt{d}}{\varepsilon}\right)$, which significantly outperforms the standard rate $\widetilde{\mathcal{O}}\left(\frac{d}{\varepsilon^2}\right)$ of vanilla diffusion models. Numerical experiments on synthetic data and the MNIST dataset validate our theoretical insights.

PDF Details

TMLR Journal 2025 Journal Article

Faithful Interpretation for Graph Neural Networks

Lijie Hu
Tianhao Huang
Lu Yu
Wanyu Lin
Tianhang Zheng
Di Wang

Currently, attention mechanisms have garnered increasing attention in Graph Neural Networks (GNNs), such as Graph Attention Networks (GATs) and Graph Transformers (GTs). This is due to not only the commendable boost in performance they offer but also their capacity to provide a more lucid rationale for model behaviors, which are often viewed as inscrutable. However, Attention-based GNNs have demonstrated instability in interpretability when subjected to various sources of perturbations during both training and testing phases, including factors like additional edges or nodes. In this paper, we propose a solution to this problem by introducing a novel notion called Faithful Graph Attention-based Interpretation (FGAI). In particular, FGAI has four crucial properties in terms of stability and sensitivity to interpretation and the final output distribution. Built upon this notion, we propose an efficient methodology for obtaining FGAI, which can be viewed as an ad hoc modification to the canonical Attention-based GNNs. To validate our proposed solution, we introduce two novel metrics tailored for graph interpretation assessment. Experimental results demonstrate that FGAI exhibits superior stability and preserves the interpretability of attention under various forms of perturbations and randomness, which makes FGAI a more faithful and reliable explanation tool.

PDF Details

YNIMG Journal 2025 Journal Article

The neural representation of body orientation and emotion from biological motion

Shuaicheng Liu
Lu Yu
Jie Ren
Mingming Zhang
Wenbo Luo

The perception of human body orientation and emotion in others provides crucial insights into their intentions. While significant research has explored the brain's representation of body orientation and emotion processing, their possible combined representation remains less well understood. In this study, functional magnetic resonance imaging was employed to investigate this issue. Participants were shown point-light displays and tasked with recognizing both body emotion and orientation. The analysis of functional activation revealed that the extrastriate body area encodesd emotion, while the precentral gyrus and postcentral gyrus encoded body orientation. Additionally, results from multivariate pattern analysis and representational similarity analysis demonstrated that the lingual gyrus, precentral gyrus, and postcentral gyrus played a critical role in processing body orientation, whereas the lingual gyrus and extrastriate body area were crucial for processing emotion. Furthermore, the commonality analysis found that the neural representations of emotion and body orientation in the lingual and precentral gyrus were not interacting, but rather competing. Lastly, a remarkable interaction between hemisphere and body orientation revealed in the connection analysis showed that the coupling between the inferior parietal lobule and the left precentral gyrus was more sensitive to a 90° body orientation, while the coupling between the inferior parietal lobule and the right precentral gyrus was sensitive to 0° and 45° body orientation. Overall, these findings suggest that the conflicted relationship between the neural representation of body orientation and emotion in LING and PreCG when point-light displays were shown, and the different hemispheres play different role in encoding different body orientations.

Details DOI

AAAI Conference 2024 Conference Paper

Fine-Grained Knowledge Selection and Restoration for Non-exemplar Class Incremental Learning

Jiang-Tian Zhai
Xialei Liu
Lu Yu
Ming-Ming Cheng

Non-exemplar class incremental learning aims to learn both the new and old tasks without accessing any training data from the past. This strict restriction enlarges the difficulty of alleviating catastrophic forgetting since all techniques can only be applied to current task data. Considering this challenge, we propose a novel framework of fine-grained knowledge selection and restoration. The conventional knowledge distillation-based methods place too strict constraints on the network parameters and features to prevent forgetting, which limits the training of new tasks. To loose this constraint, we proposed a novel fine-grained selective patch-level distillation to adaptively balance plasticity and stability. Some task-agnostic patches can be used to preserve the decision boundary of the old task. While some patches containing the important foreground are favorable for learning the new task. Moreover, we employ a task-agnostic mechanism to generate more realistic prototypes of old tasks with the current task sample for reducing classifier bias for fine-grained knowledge restoration. Extensive experiments on CIFAR100, TinyImageNet and ImageNet-Subset demonstrate the effectiveness of our method. Code is available at https://github.com/scok30/vit-cil.

PDF Details DOI

ICLR Conference 2024 Conference Paper

Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited

Lu Yu
Avetik G. Karagulyan
Arnak S. Dalalyan

We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in $\mathbb{R}^p$. In this context, if no additional density information is available, the randomized midpoint discretization for the kinetic Langevin diffusion is known to be the most scalable method in high dimensions with large condition numbers. Our main result is a nonasymptotic and easy to compute upper bound on the $W_2$-error of this method. To provide a more thorough explanation of our method for establishing the computable upper bound, we conduct an analysis of the midpoint discretization for the vanilla Langevin process. This analysis helps to clarify the underlying principles and provides valuable insights that we use to establish an improved upper bound for the kinetic Langevin process with the midpoint discretization. Furthermore, by applying these techniques we establish new guarantees for the kinetic Langevin process with Euler discretization, which have a better dependence on the condition number than existing upper bounds

Details

NeurIPS Conference 2024 Conference Paper

Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models

Lu Yu
Haiyang Zhang
Changsheng Xu

Due to the impressive zero-shot capabilities, pre-trained vision-language models (e. g. CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in text-guided attention. Building upon this observation, we propose a simple yet effective strategy: Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR). This framework incorporates two components: the Attention Refinement module and the Attention-based Model Constraint module. Our goal is to maintain the generalization of the CLIP model and enhance its adversarial robustness: The Attention Refinement module aligns the text-guided attention obtained from the target model via adversarial examples with the text-guided attention acquired from the original model via clean examples. This alignment enhances the model’s robustness. Additionally, the Attention-based Model Constraint module acquires text-guided attention from both the target and original models using clean examples. Its objective is to maintain model performance on clean samples while enhancing overall robustness. The experiments validate that our method yields a 9. 58% enhancement in zero-shot robust accuracy over the current state-of-the-art techniques across 16 datasets. Our code is available at https: //github. com/zhyblue424/TGA-ZSR.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Towards Multi-dimensional Explanation Alignment for Medical Classification

Lijie Hu
Songning Lai
Wenshuo Chen
Hongru Xiao
Hongbin Lin
Lu Yu
Jingfeng Zhang
Di Wang

The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several challenges, including dependency on specific models, difficulties in understanding and visualization, and issues related to efficiency. To address these limitations, we propose a novel framework called Med-MICN (Medical Multi-dimensional Interpretable Concept Network). Med-MICN provides interpretability alignment for various angles, including neural symbolic reasoning, concept semantics, and saliency maps, which are superior to current interpretable methods. Its advantages include high prediction accuracy, interpretability across multiple dimensions, and automation through an end-to-end concept labeling process that reduces the need for extensive human training effort when working with new datasets. To demonstrate the effectiveness and interpretability of Med-MICN, we apply it to four benchmark datasets and compare it with baselines. The results clearly demonstrate the superior performance and interpretability of our Med-MICN.

PDF Details DOI

EAAI Journal 2023 Journal Article

CGINet: Cross-modality grade interaction network for RGB-T crowd counting

Yi Pan
Wujie Zhou
Xiaohong Qian
Shanshan Mao
Rongwang Yang
Lu Yu

Crowd counting is a fundamental and challenging task that requires rich information to generate a pixel-level crowd density map. Additionally, the development of thermal sensing and its applicability to computer vision has enabled the use of thermal information for crowd counting. Considering the complementary characteristics of RGB (red–green–blue) and thermal images in different feature encoding stages, we propose a cross-modality grade interaction network (CGINet) for RGB-T (RGB and thermal) crowd counting. We introduce an RGB cooperative enhancement module for thermal information to correctly extract low-level features from scenes containing objects with different scales. As RGB information is sensitive to lighting and occlusion while extracting high-level features, we propose a thermal information supplementary module to increase the RGB feature robustness. In addition, a novel multilayer decoding module fully integrates features at different levels, exploits the features of different layers, and predicts the crowd density map. Results from comprehensive experiments on the RGBT-CC benchmark demonstrate the effectiveness of the proposed CGINet for RGB-T crowd counting. In addition, CGINet achieves excellent results on the ShanghaiTechRGB dataset containing paired RGB images and depth maps. The experimental results highlight the advanced architecture and generalization ability of CGINet for multimodality crowd counting.

Details DOI

EAAI Journal 2023 Journal Article

DRNet: Dual-stage refinement network with boundary inference for RGB-D semantic segmentation of indoor scenes

Enquan Yang
Wujie Zhou
Xiaohong Qian
Jingsheng Lei
Lu Yu

Semantic segmentation is a dense pixel prediction task, and its accuracy depends on the extraction of long-range contextual knowledge and refinement of segmentation boundaries. Most segmentation methods are based on feature extraction using a convolutional neural network, and layer-by-layer sampling and fusion are applied to solve inherent problems such as chaotic boundaries and scattered objects. Owing to the limited receptive field and loss of details during downsampling, the segmentation results may be unsatisfactory. To address existing shortcomings, we propose a dual-stage refinement network (DRNet) for semantic segmentation. In the first stage, we adopt an efficient spatiotemporal representation learning framework called UniFormer. We also use a novel boundary extractor and initial segmentation map generator to obtain rough segmentation results. In the second stage, we use the rough segmentation map and extracted boundary information in a graph reasoning module that restores the class boundary features while completing global modeling and local information inference. Benefiting from the acquisition of long-range dependencies between image pixels, contextual information promotes the distinction of pixel categories. In addition, edge information can increase the interclass distinguishability and refine the segmentation boundaries. Results from extensive experiments demonstrate that the proposed DRNet outperforms state-of-the-art semantic segmentation methods. The codes and results are available at: https: //github. com/EnquanYang2022/DRNet.

Details DOI

EAAI Journal 2023 Journal Article

Global contextually guided lightweight network for RGB-thermal urban scene understanding

Tingting Gong
Wujie Zhou
Xiaohong Qian
Jingsheng Lei
Lu Yu

Recent achievements in scene understanding have benefited considerably from the rapid development of convolutional neural networks. However, improvements of scene understanding methods have been restricted in terms of practical deployment, especially in mobile devices, owing to their high computational costs and memory consumption. Existing networks can integrate RGB and thermal (RGB-T) cues for sample fusion, resulting in insufficient exploitation of the complicated correlations between the two image modalities. Moreover, some of these methods do not consider the influence of global features on the interactions between low- and high-level features. Hence, in this study, we introduce a novel network named the global contextually guided lightweight network (GCGLNet), which has fewer parameters and higher speed, ensuring accuracy. Specifically, secondary cross-modal integration is introduced to remove redundant information while fusing and propagating effective modal information. A hybrid feature-cascaded aggregation module is also introduced to emphasize the global context along with complementation and calibration between the high- and low-level features. Extensive experiments were conducted on two benchmark RGB-T datasets to demonstrate that the proposed GCGLNet yields an accuracy comparable with those of state-of-the-art approaches when operated at 51. 89 FPS for 480 × 640 pixel inputs with only 7. 87 M parameters. Thus, GCGLNet is expected to open new avenues for research on urban scene understanding via RGB-T sensors.

Details DOI

IJCAI Conference 2023 Conference Paper

Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment

Lu Yu
Malvina Nikandrou
Jiali Jin
Verena Rieser

Automated image captioning has the potential to be a useful tool for people with vision impairments. Images taken by this user group are often noisy, which leads to incorrect and even unsafe model predictions. In this paper, we propose a quality-agnostic framework to improve the performance and robustness of image captioning models for visually impaired people. We address this problem from three angles: data, model, and evaluation. First, we show how data augmentation techniques for generating synthetic noise can address data sparsity in this domain. Second, we enhance the robustness of the model by expanding a state-of-the-art model to a dual network architecture, using the augmented data and leveraging different consistency losses. Our results demonstrate increased performance, e. g. an absolute improvement of 2. 15 on CIDEr, compared to state-of-the-art image captioning networks, as well as increased robustness to noise with up to 3 points improvement on CIDEr in more noisy settings. Finally, we evaluate the prediction reliability using confidence calibration on images with different difficulty / noise levels, showing that our models perform more reliably in safety-critical situations. The improved model is part of an assisted living application, which we develop in partnership with the Royal National Institute of Blind People.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

Volume Feature Rendering for Fast Neural Radiance Field Reconstruction

Kang Han
Wei Xiang
Lu Yu

Neural radiance fields (NeRFs) are able to synthesize realistic novel views from multi-view images captured from distinct positions and perspectives. In NeRF's rendering pipeline, neural networks are used to represent a scene independently or transform queried learnable feature vector of a point to the expected color or density. With the aid of geometry guides either in the form of occupancy grids or proposal networks, the number of color neural network evaluations can be reduced from hundreds to dozens in the standard volume rendering framework. However, many evaluations of the color neural network are still a bottleneck for fast NeRF reconstruction. This paper revisits volume feature rendering (VFR) for the purpose of fast NeRF reconstruction. The VFR integrates the queried feature vectors of a ray into one feature vector, which is then transformed to the final pixel color by a color neural network. This fundamental change to the standard volume rendering framework requires only one single color neural network evaluation to render a pixel, which substantially lowers the high computational complexity of the rendering framework attributed to a large number of color neural network evaluations. Consequently, we can use a comparably larger color neural network to achieve a better rendering quality while maintaining the same training and rendering time costs. This approach achieves the state-of-the-art rendering quality on both synthetic and real-world datasets while requiring less training time compared with existing methods.

PDF Details

AAAI Conference 2022 Conference Paper

SAIL: Self-Augmented Graph Contrastive Learning

Lu Yu
Shichao Pei
Lizhong Ding
Jun Zhou
Longfei Li
Chuxu Zhang
Xiangliang Zhang

This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined. The performance of GNNs depends on both the node feature smoothness and the locality of graph structure. To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed SAIL - a novel Self-Augmented graph contrastive Learning framework, with two complementary self-distilling regularization modules, i. e. , intra- and inter-graph knowledge distillation. We demonstrate the competitive performance of SAIL on a variety of graph applications. Even with a single GNN layer, SAIL has consistently competitive or even better performance on various benchmark datasets, comparing with state-of-the-art baselines.

PDF Details

NeurIPS Conference 2021 Conference Paper

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias

Lu Yu
Krishnakumar Balasubramanian
Stanislav Volgushev
Murat A. Erdogdu

Structured non-convex learning problems, for which critical points have favorable statistical properties, arise frequently in statistical machine learning. Algorithmic convergence and statistical estimation rates are well-understood for such problems. However, quantifying the uncertainty associated with the underlying training algorithm is not well-studied in the non-convex setting. In order to address this shortcoming, in this work, we establish an asymptotic normality result for the constant step size stochastic gradient descent (SGD) algorithm---a widely used algorithm in practice. Specifically, based on the relationship between SGD and Markov Chains [DDB19], we show that the average of SGD iterates is asymptotically normally distributed around the expected value of their unique invariant distribution, as long as the non-convex and non-smooth objective function satisfies a dissipativity property. We also characterize the bias between this expected value and the critical points of the objective function under various local regularity conditions. Together, the above two results could be leveraged to construct confidence intervals for non-convex problems that are trained using the SGD algorithm.

PDF Details

NeurIPS Conference 2020 Conference Paper

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

Yaxing Wang
Lu Yu
Joost van de Weijer

Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the bottom layers and (b) semantic information extracted from the top layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i. e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes.

PDF Details

IJCAI Conference 2019 Conference Paper

Improving Cross-lingual Entity Alignment via Optimal Transport

Shichao Pei
Lu Yu
Xiangliang Zhang

Cross-lingual entity alignment identifies entity pairs that share the same meanings but locate in different language knowledge graphs (KGs). The study in this paper is to address two limitations that widely exist in current solutions: 1) the alignment loss functions defined at the entity level serve well the purpose of aligning labeled entities but fail to match the whole picture of labeled and unlabeled entities in different KGs; 2) the translation from one domain to the other has been considered (e. g. , X to Y by M1 or Y to X by M2). However, the important duality of alignment between different KGs (X to Y by M1 and Y to X by M2) is ignored. We propose a novel entity alignment framework (OTEA), which dually optimizes the entity-level loss and group-level loss via optimal transport theory. We also impose a regularizer on the dual translation matrices to mitigate the effect of noise during transformation. Extensive experimental results show that our model consistently outperforms the state-of-the-arts with significant improvements on alignment accuracy.

PDF Details

AAAI Conference 2019 Conference Paper

Multi-Order Attentive Ranking Model for Sequential Recommendation

Lu Yu
Chuxu Zhang
Shangsong Liang
Xiangliang Zhang

In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https: //github. com/voladorlu/MARank.

PDF Details

IJCAI Conference 2018 Conference Paper

Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference

Chuxu Zhang
Lu Yu
Xiangliang Zhang
Nitesh V. Chawla

We study the problem of author-paper correlation inference in big scholarly data, which is to effectively infer potential correlated works for researchers using historical records. Unlike supervised learning algorithms that predict relevance score of author-paper pair via time and memory consuming feature engineering, network embedding methods automatically learn nodes' representations that can be further used to infer author-paper correlation. However, most current models suffer from two limitations: (1) they produce general purpose embeddings that are independent of the specific task; (2) they are usually based on network structure but out of content semantic awareness. To address these drawbacks, we propose a task-guided and semantic-aware ranking model. First, the historical interactions among all correlated author-paper pairs are formulated as a pairwise ranking loss. Next, the paper's semantic embedding encoded by gated recurrent neural network, together with the author's latent feature is used to score each author-paper pair in ranking loss. Finally, a heterogeneous relations integrative learning module is designed to further augment the model. The evaluation results of extensive experiments on the well known AMiner dataset demonstrate that the proposed model reaches significant better performance, comparing to a number of baselines.

PDF Details

AAAI Conference 2018 Conference Paper

WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation

Lu Yu
Chuxu Zhang
Shichao Pei
Guolei Sun
Xiangliang Zhang

Top-N item recommendation techniques, e. g. , pairwise models, learn the rank of users’ preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. This separation results in an important issue: the extreme imbalance between positive and negative samples, because the number of items with user actions is much less than those without actions. The problem is even worse for “cold-start” users. In addition, existing learning models only consider the observed user-item proximity, while neglecting other useful relations, such as the unobserved but potentially helpful user-item relations, and highorder proximity in user-user, item-item relations. In this paper, we aim at incorporating multiple types of user-item relations into a uniﬁed pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR). Instead of taking statical separation of positive and negative sets, we employ a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efﬁciently learning the proposed pairwise ranking model. The proposed method is compared with several state-of-the-art baselines on two large and sparse datasets. Experimental results show that our proposed model outperforms the other baselines with average 4% at different top-N metrics, in particular for coldstart users with 6% on average.

PDF Details