130 citations · 486 across the 38 of their papers we have counts for
10 papers · 1 filter
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
Chong Liu, Xiaoyang Liu, Rongqin Zheng +6
Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and…
Contrastive Cross-domain Recommendation in Matching
Ruobing Xie, Qi Liu, Liangdong Wang +3
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-w…
Personalized Transfer of User Preferences for Cross-domain Recommendation
Yongchun Zhu, Zhenwei Tang, Yudan Liu +5
Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-st…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang +5
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem.…
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang +5
Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich info…
Long Short-Term Temporal Meta-learning in Online Recommendation
Ruobing Xie, Yalong Wang, Rui Wang +4
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task)…
Understanding WeChat User Preferences and "Wow" Diffusion
Fanjin Zhang, Jie Tang, Xueyi Liu +9
WeChat is the largest social instant messaging platform in China, with 1.1 billion monthly active users. "Top Stories" is a novel friend-enhanced recommendation engine in WeChat, i…
UPRec: User-Aware Pre-training for Recommender Systems
Chaojun Xiao, Ruobing Xie, Yuan Yao +4
Existing sequential recommendation methods rely on large amounts of training data and usually suffer from the data sparsity problem. To tackle this, the pre-training mechanism has…
Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
Ruobing Xie, Qi Liu, Shukai Liu +4
Recently, real-world recommendation systems need to deal with millions of candidates. It is extremely challenging to conduct sophisticated end-to-end algorithms on the entire corpu…