11 citations · 14 across the 14 of their papers we have counts for
14 papers
From Heuristics to Transformers: A Comprehensive Survey of Type Inference from Stripped Binaries
Hua Zheng, Yuhang Guo, Kuanishbay Sadatdiynov +5
The recovery of high-level type information from stripped binaries-executables devoid of symbol tables and debugging information-is a cornerstone of software reverse engineering, v…
Large Language Models for Multilingual Code Intelligence: A Survey
Chao Jiang, Dugang Liu, Cheng Wen +6
Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance…
Enhancing guidance for missing data in diffusion-based sequential recommendation
Qilong Yan, Yifei Xing, Dugang Liu +2
Contemporary sequential recommendation methods are becoming more complex, shifting from classification to a diffusion-guided generative paradigm. However, the quality of guidance i…
See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
Pengteng Li, Pinhao Song, Wuyang Li +5
We introduce SEE&TREK, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMS) under vision-only const…
Benchmarking for Deep Uplift Modeling in Online Marketing
Dugang Liu, Xing Tang, Yang Qiao +4
Online marketing is critical for many industrial platforms and business applications, aiming to increase user engagement and platform revenue by identifying corresponding delivery-…
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
Dugang Liu, Shenxian Xian, Xiaolin Lin +5
The training paradigm integrating large language models (LLM) is gradually reshaping sequential recommender systems (SRS) and has shown promising results. However, most existing LL…
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions
Lei Li, Yongfeng Zhang, Dugang Liu +1
Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender…
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction
Xing Tang, Yang Qiao, Yuwen Fu +3
A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a unified click-through rate (CTR) prediction model to serve them si…
Explicit Feature Interaction-aware Uplift Network for Online Marketing
Dugang Liu, Xing Tang, Han Gao +2
As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts,…
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
Dugang Liu, Pengxiang Cheng, Zinan Lin +6
Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an…
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Dugang Liu, Yang Qiao, Xing Tang +4
Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most o…
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Fuyuan Lyu, Xing Tang, Dugang Liu +4
Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneous…
Optimizing Feature Set for Click-Through Rate Prediction
Fuyuan Lyu, Xing Tang, Dugang Liu +3
Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Ther…
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Dugang Liu, Pengxiang Cheng, Hong Zhu +6
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications l…