Search Results for author:
Found 31 papers, 17 papers with code
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation
Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization).
Multi-level Chaotic Maps for 3D Textured Model Encryption
In this work, a multi-level chaotic maps models for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons and textures.
A Hierarchical Attention Model for Social Contextual Image Recommendation
After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects.
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks.
Quality-aware Unpaired Image-to-Image Translation
To tackle the above two challenges, in this paper, we propose a unified quality-aware GAN-based framework for unpaired image-to-image translation, where a quality-aware loss is explicitly incorporated by comparing each source image and the reconstructed image at the domain level.
A Neural Influence Diffusion Model for Social Recommendation
The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.
Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).
Ranked #1 on
Recommendation Systems
on Amazon Fashion
(nDCG@10 (500 Neg. Samples) metric, using extra
training data)
Personalized Multimedia Item and Key Frame Recommendation
We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.
Aesthetic Attributes Assessment of Images
This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Recently, we propose a preliminary work of a neural influence diffusion network (i. e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user.
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data.
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase.
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
Therefore, item recommendation and attribute inference have become two main tasks in these platforms.
R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences.
Learning Fair Representations for Recommendation: A Graph-based Perspective
For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user.
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user.
Privileged Graph Distillation for Cold Start Recommendation
The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching
In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.
ProFairRec: Provider Fairness-aware News Recommendation
To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data.
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"
A Review-aware Graph Contrastive Learning Framework for Recommendation
Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences.
Improving Recommendation Fairness via Data Augmentation
We augment imbalanced training data towards balanced data distribution to improve fairness.
Data Augmentation for Seizure Prediction with Generative Diffusion Model
Data augmentation is an intuitive way to solve this problem.
Description-Enhanced Label Embedding Contrastive Learning for Text Classification
Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.
Generative Contrastive Graph Learning for Recommendation
Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model.
