13 citations · 15 across the 5 of their papers we have counts for
6 papers
KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation
Jiangxia Cao, Hao Peng, Wenlong Xu +97
Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recom…
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling
Heng Chang, Liang Gu, Cheng Hu +5
Cross-domain recommendation (CDR) is a task that aims to improve the recommendation performance in a target domain by leveraging the information from source domains. Contrastive le…
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…
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
Peijie Sun, Yifan Wang, Min Zhang +5
With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable na…
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Hong Zhu, Runpeng Yu, Xing Tang +3
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training. In…
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Chuhan Wu, Jingjie Li, Qinglin Jia +3
Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of cons…