297 citations · 2.2k across the 85 of their papers we have counts for
17 papers · 1 filter
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 Deep Residual Reasoning for Temporal Moment Localization
Ziyang Ma, Xianjing Han, Xuemeng Song +2
Temporal Moment Localization (TML) in untrimmed videos is a challenging task in the field of multimedia, which aims at localizing the start and end points of the activity in the vi…
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…
Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
Zongmeng Zhang, Xianjing Han, Xuemeng Song +2
This paper focuses on tackling the problem of temporal language localization in videos, which aims to identify the start and end points of a moment described by a natural language…
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…
Lipschitz Continuity Guided Knowledge Distillation
Yuzhang Shang, Bin Duan, Ziliang Zong +2
Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones. Although grea…
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…
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
Fangkai Jiao, Yangyang Guo, Yilin Niu +3
Pre-trained Language Models (PLMs) have achieved great success on Machine Reading Comprehension (MRC) over the past few years. Although the general language representation learned…
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
Yangyang Guo, Liqiang Nie, Zhiyong Cheng +3
A number of studies point out that current Visual Question Answering (VQA) models are severely affected by the language prior problem, which refers to blindly making predictions ba…
A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering
Yongqi Li, Wenjie Li, Liqiang Nie
In recent years, conversational agents have provided a natural and convenient access to useful information in people's daily life, along with a broad and new research topic, conver…
Graph Contrastive Clustering
Huasong Zhong, Jianlong Wu, Chong Chen +5
Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, thes…
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…
Answer Questions with Right Image Regions: A Visual Attention Regularization Approach
Yibing Liu, Yangyang Guo, Jianhua Yin +3
Visual attention in Visual Question Answering (VQA) targets at locating the right image regions regarding the answer prediction, offering a powerful technique to promote multi-moda…
Incremental Knowledge Based Question Answering
Yongqi Li, Wenjie Li, Liqiang Nie
In the past years, Knowledge-Based Question Answering (KBQA), which aims to answer natural language questions using facts in a knowledge base, has been well developed. Existing app…