297 citations · 2.2k across the 85 of their papers we have counts for
25 papers · 1 filter
Semantic-Guided Feature Distillation for Multimodal Recommendation
Fan Liu, Huilin Chen, Zhiyong Cheng +2
Multimodal recommendation exploits the rich multimodal information associated with users or items to enhance the representation learning for better performance. In these methods, e…
HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation
Han Liu, Yinwei Wei, Jianhua Yin +1
An efficient solution to the large-scale recommender system is to represent users and items as binary hash codes in the Hamming space. Towards this end, existing methods tend to co…
Causal Inference for Knowledge Graph based Recommendation
Yinwei Wei, Xiang Wang, Liqiang Nie +3
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model. By mapping items with the entities in K…
Multimodal Matching-aware Co-attention Networks with Mutual Knowledge Distillation for Fake News Detection
Linmei Hu, Ziwang Zhao, Weijian Qi +2
Fake news often involves multimedia information such as text and image to mislead readers, proliferating and expanding its influence. Most existing fake news detection methods appl…
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
Fan Liu, Zhiyong Cheng, Huilin Chen +3
Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when c…
User-controllable Recommendation Against Filter Bubbles
Wenjie Wang, Fuli Feng, Liqiang Nie +1
Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along…
Image-text Retrieval: A Survey on Recent Research and Development
Min Cao, Shiping Li, Juntao Li +2
In the past few years, cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world…
Disentangled Multimodal Representation Learning for Recommendation
Fan Liu, Huilin Chen, Zhiyong Cheng +3
Many multimodal recommender systems have been proposed to exploit the rich side information associated with users or items (e.g., user reviews and item images) for learning better…
Learning Robust Recommender from Noisy Implicit Feedback
Wenjie Wang, Fuli Feng, Xiangnan He +2
The ubiquity of implicit feedback makes it indispensable for building recommender systems. However, it does not actually reflect the actual satisfaction of users. For example, in E…
GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Wei Yinwei, Wang Xiang, Nie Liqiang +2
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interac…
Hierarchical User Intent Graph Network forMultimedia Recommendation
Wei Yinwei, Wang Xiang, He Xiangnan +3
In this work, we aim to learn multi-level user intents from the co-interacted patterns of items, so as to obtain high-quality representations of users and items and further enhance…
When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
Xiangkun Yin, Yangyang Guo, Liqiang Nie +1
Personalization lies at the core of boosting the product search system performance. Prior studies mainly resorted to the semantic matching between textual queries and user/product…
Contrastive Learning for Cold-Start Recommendation
Yinwei Wei, Xiang Wang, Qi Li +4
Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use co…
Review Polarity-wise Recommender
Han Liu, Yangyang Guo, Jianhua Yin +2
Utilizing review information to enhance recommendation, the de facto review-involved recommender systems, have received increasing interests over the past few years. Thereinto, one…
Interest-aware Message-Passing GCN for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the col…
Feature-level Attentive ICF for Recommendation
Zhiyong Cheng, Fan Liu, Shenghan Mei +3
Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender sys…
Market2Dish: Health-aware Food Recommendation
Wenjie Wang, Ling-yu Duan, Hao Jiang +3
With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention. However, most existing food-related research efforts focu…
Enhancing Factorization Machines with Generalized Metric Learning
Yangyang Guo, Zhiyong Cheng, Jiazheng Jing +3
Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the…
Denoising Implicit Feedback for Recommendation
Wenjie Wang, Fuli Feng, Xiangnan He +2
The ubiquity of implicit feedback makes them the default choice to build online recommender systems. While the large volume of implicit feedback alleviates the data sparsity issue,…
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
As important side information, attributes have been widely exploited in the existing recommender system for better performance. In the real-world scenarios, it is common that some…
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
Fan Liu, Zhiyong Cheng, Changchang Sun +3
Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. How…
Attentive Long Short-Term Preference Modeling for Personalized Product Search
Yangyang Guo, Zhiyong Cheng, Liqiang Nie +3
E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-te…
Discrete Factorization Machines for Fast Feature-based Recommendation
Han Liu, Xiangnan He, Fuli Feng +3
User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expe…
Neural Collaborative Filtering
Xiangnan He, Lizi Liao, Hanwang Zhang +3
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural n…