62 citations · 130 across the 10 of their papers we have counts for
8 papers
GRN: Generative Rerank Network for Context-wise Recommendation
Yufei Feng, Binbin Hu, Yu Gong +3
Reranking is attracting incremental attention in the recommender systems, which rearranges the input ranking list into the final rank-ing list to better meet user demands. Most exi…
Revisit Recommender System in the Permutation Prospective
Yufei Feng, Yu Gong, Fei Sun +2
Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite cand…
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Runsheng Yu, Yu Gong, Xu He +4
A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data c…
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Yufei Feng, Fuyu Lv, Binbin Hu +5
Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences…
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Yufei Feng, Binbin Hu, Fuyu Lv +3
Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts m…
EdgeRec: Recommender System on Edge in Mobile Taobao
Yu Gong, Ziwen Jiang, Yufei Feng +4
Recommender system (RS) has become a crucial module in most web-scale applications. Recently, most RSs are in the waterfall form based on the cloud-to-edge framework, where recomme…