262 citations · 1.1k across the 41 of their papers we have counts for
7 papers · 1 filter
CLEAR: Contrastive Learning for Sentence Representation
Zhuofeng Wu, Sinong Wang, Jiatao Gu +3
Pre-trained language models have proven their unique powers in capturing implicit language features. However, most pre-training approaches focus on the word-level training objectiv…
Graph Neural Networks in Recommender Systems: A Survey
Shiwen Wu, Fei Sun, Wentao Zhang +2
With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender s…
Contrastive Learning for Sequential Recommendation
Xu Xie, Fei Sun, Zhaoyang Liu +4
Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactio…
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
Fuyu Lv, Mengxue Li, Tonglei Guo +4
Deep learning-based sequential recommender systems have recently attracted increasing attention from both academia and industry. Most of industrial Embedding-Based Retrieval (EBR)…
Ranking Enhanced Dialogue Generation
Changying Hao, Liang Pang, Yanyan Lan +3
How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., re…
Learning Personalized Risk Preferences for Recommendation
Yingqiang Ge, Shuyuan Xu, Shuchang Liu +3
The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and revie…
Understanding Echo Chambers in E-commerce Recommender Systems
Yingqiang Ge, Shuya Zhao, Honglu Zhou +4
Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper item…