75 citations · 222 across the 25 of their papers we have counts for
25 papers
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
Hengchang Hu, Wei Guo, Yong Liu +1
In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse mo…
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Hengyu Zhang, Chang Meng, Wei Guo +5
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks. User be…
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
Jianghao Lin, Yanru Qu, Wei Guo +4
With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features…
ATWM: Defense against adversarial malware based on adversarial training
Kun Li, Fan Zhang, Wei Guo
Deep learning technology has made great achievements in the field of image. In order to defend against malware attacks, researchers have proposed many Windows malware detection mod…
How to Find Opinion Leader on the Online Social Network?
Bailu Jin, Mengbang Zou, Zhuangkun Wei +1
Online social networks (OSNs) provide a platform for individuals to share information, exchange ideas, and build social connections beyond in-person interactions. For a specific to…
FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign
Kun Li, Fan Zhang, Wei Guo
Malware detection models based on deep learning have been widely used, but recent research shows that deep learning models are vulnerable to adversarial attacks. Adversarial attack…
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
Zhen Bi, Jing Chen, Yinuo Jiang +4
Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specificati…
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
Wei Guo, Chang Meng, Enming Yuan +8
Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-fa…
A Survey on User Behavior Modeling in Recommender Systems
Zhicheng He, Weiwen Liu, Wei Guo +4
User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and…
Editing Language Model-based Knowledge Graph Embeddings
Siyuan Cheng, Ningyu Zhang, Bozhong Tian +3
Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deploy…
Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids Analysis
Wei Guo, Benedetta Tondi, Mauro Barni
We propose a Universal Defence against backdoor attacks based on Clustering and Centroids Analysis (CCA-UD). The goal of the defence is to reveal whether a Deep Neural Network mode…
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Haolun Wu, Yingxue Zhang, Chen Ma +4
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods f…
A Survey on Reinforcement Learning in Aviation Applications
Pouria Razzaghi, Amin Tabrizian, Wei Guo +6
Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-…
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
Xiangyang Li, Bo Chen, HuiFeng Guo +10
Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the \textbf{two-tow…
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Hengyu Zhang, Enming Yuan, Wei Guo +6
Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing beha…
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
Chang Meng, Ziqi Zhao, Wei Guo +6
Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-fac…
Data Driven Modeling Social Media Influence using Differential Equations
Bailu Jin, Weisi Guo
Individuals modify their opinions towards a topic based on their social interactions. Opinion evolution models conceptualize the change of opinion as a uni-dimensional continuum, a…
Improved-Flow Warp Module for Remote Sensing Semantic Segmentation
Yinjie Zhang, Yi Liu, Wei Guo
Remote sensing semantic segmentation aims to assign automatically each pixel on aerial images with specific label. In this letter, we proposed a new module, called improved-flow wa…
Cross Pairwise Ranking for Unbiased Item Recommendation
Qi Wan, Xiangnan He, Xiang Wang +3
Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The lo…
Rotated Object Detection via Scale-invariant Mahalanobis Distance in Aerial Images
Siyang Wen, Wei Guo, Yi Liu +1
Rotated object detection in aerial images is a meaningful yet challenging task as objects are densely arranged and have arbitrary orientations. The eight-parameter (coordinates of…
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
Wei Guo, Can Zhang, Zhicheng He +6
CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on c…
The Rising Entropy of English in the Attention Economy
Charlie Pilgrim, Weisi Guo, Thomas T. Hills
We present evidence that the word entropy of American English has been rising steadily since around 1900, contrary to predictions from existing sociolinguistic theories. We also fi…
Dual Graph enhanced Embedding Neural Network for CTR Prediction
Wei Guo, Rong Su, Renhao Tan +5
CTR prediction, which aims to estimate the probability that a user will click an item, plays a crucial role in online advertising and recommender system. Feature interaction modeli…
Deep Learning for Click-Through Rate Estimation
Weinan Zhang, Jiarui Qin, Wei Guo +2
Click-through rate (CTR) estimation plays as a core function module in various personalized online services, including online advertising, recommender systems, and web search etc.…