Jun Gao

Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation

2020 · Complexity

Modeling user behaviors as sequential learning provides key advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for the purpose of personalized search …

Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations

2021

Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label, and have achieved promising results. However, the emotion cause, an essential …

Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Zero-shot cross-lingual event argument extraction (EAE) is a challenging yet practical problem in Information Extraction. Most previous works heavily rely on external structured linguistic features, which are not easily accessible in real-world scenarios. This paper …

Contrastive Learning with High-Quality and Low-Quality Augmented Data for Query-Focused Summarization

2024

Unlike general text summarization, Query-focused summarization (QFS) is severely limited by insufficient datasets, forcing previous research to transform datasets from other tasks into QFS format for data augmentation. However, this approach has resulted in two …

Two-Stage Feature Generation with Transformer and Reinforcement Learning

2025 · arXiv (Cornell University)

Feature generation is a critical step in machine learning, aiming to enhance model performance by capturing complex relationships within the data and generating meaningful new features. Traditional feature generation methods heavily rely on domain expertise …

ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

2017 · arXiv (Cornell University)

A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, …

ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, …