11 citations · 14 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…