Search Results for author:
Found 44 papers, 29 papers with code
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items.
Cluster-based Graph Collaborative Filtering
This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them.
Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models
Recommendation systems harness user-item interactions like clicks and reviews to learn their representations.
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation
To obtain robust and independent representations for each factor associated with a specific attribute, we first disentangle the representations of features both within and across different modalities.
Prior-Free Continual Learning with Unlabeled Data in the Wild
Existing CL methods usually reduce forgetting with task priors, \ie using task identity or a subset of previously seen samples for model training.
Semantic-Guided Feature Distillation for Multimodal Recommendation
The teacher model first extracts rich modality features from the generic modality feature by considering both the semantic information of items and the complementary information of multiple modalities.
Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration
Training an effective video action recognition model poses significant computational challenges, particularly under limited resource budgets.
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.
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
In this paper, we propose MB-HGCN, a novel multi-behavior recommendation model that uses a hierarchical graph convolutional network to learn user and item embeddings from coarse-grained on the global level to fine-grained on the behavior-specific level.
Continual Learning with Strong Experience Replay
Experience Replay (ER) is a simple and effective rehearsal-based strategy, which optimizes the model with current training data and a subset of old samples stored in a memory buffer.
Multi-Behavior Recommendation with Cascading Graph Convolution Networks
Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning.
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences.
Temporal Action Localization with Multi-temporal Scales
However, the temporal features of a low-level scale lack enough semantics for action classification while a high-level scale cannot provide rich details of the action boundaries.
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA
On the other hand, pertaining to the implicit knowledge, the multi-modal implicit knowledge for knowledge-based VQA still remains largely unexplored.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.
Disentangled Multimodal Representation Learning for Recommendation
However, existing methods ignore the fact that different modalities contribute differently towards a user's preference on various factors of an item.
On Modality Bias Recognition and Reduction
From the results on four datasets regarding the above three tasks, our method yields remarkable performance improvements compared with the baselines, demonstrating its superiority on reducing the modality bias problem.
Disentangled Graph Neural Networks for Session-based Recommendation
The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.
A Novel Patch Convolutional Neural Network for View-based 3D Model Retrieval
Thus, we tackle this problem from the perspective of exploiting the relationships between patch features to capture long-range associations among multi-view images.
When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
In addition, we empirically prove that collaborative filtering and semantic matching are complementary to each other in product search performance enhancement.
TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization
In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework.
UGRec: Modeling Directed and Undirected Relations for Recommendation
In particular, for a directed relation, we transform the head and tail entities into the corresponding relation space to model their relation; and for an undirected co-occurrence relation, we project head and tail entities into a unique hyperplane in the entity space to minimize their distance.
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
Experimental results demonstrate that our adapted margin cosine loss can greatly enhance the baseline models with an absolute performance gain of 15\% on average, strongly verifying the potential of tackling the language prior problem in VQA from the angle of the answer feature space learning.
Feature-level Attentive ICF for Recommendation
To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models.
Interest-aware Message-Passing GCN for Recommendation
To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure.
Liquidation, Leverage and Optimal Margin in Bitcoin Futures Markets
Using the generalized extreme value theory to characterize tail distributions, we address liquidation, leverage, and optimal margins for bitcoin long and short futures positions.
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
Concretely, we design a novel interpretation scheme whereby the loss of mis-predicted frequent and sparse answers of the same question type is distinctly exhibited during the late training phase.
Frame-wise Cross-modal Matching for Video Moment Retrieval
Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.
Enhancing Factorization Machines with Generalized Metric Learning
Traditional FMs adopt the inner product to model the second-order interactions between different attributes, which are represented via feature vectors.
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes.
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations.
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Considering the fact that for different users, the attributes of an item have different influence on their preference for this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information.
Fast Video Object Segmentation via Mask Transfer Network
To overcome this limitation, we propose a novel mask transfer network (MTN), which can greatly boost the processing speed of VOS and also achieve a reasonable accuracy.
Explainable Video Action Reasoning via Prior Knowledge and State Transitions
Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans.
Personalized Hashtag Recommendation for Micro-videos
The hashtags, that a user provides to a post (e. g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in.
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items.
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering
Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years.
Compositional Coding for Collaborative Filtering
However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data.
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework.
Discrete Optimal Graph Clustering
2) the relaxing process of cluster labels may cause significant information loss.
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.
Attentive Aspect Modeling for Review-aware Recommendation
The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product.
Unsupervised Online Video Object Segmentation with Motion Property Understanding
Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset.
Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation
In this paper, we investigate the capability of BMNNs using the EBP algorithm on multiclass image classification tasks.
