6 citations · 10 across the 24 of their papers we have counts for
5 papers · 1 filter
Adaptive Conditional Expert Selection Network for Multi-domain Recommendation
Kuiyao Dong, Xingyu Lou, Feng Liu +4
Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) due to its powerful expressive ability. However, such MOE-based method typic…
DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
Heyuan Huang, Xingyu Lou, Chaochao Chen +5
Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an i…
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction
Xingyu Lou, Yu Yang, Kuiyao Dong +5
As the recommendation service needs to address increasingly diverse distributions, such as multi-population, multi-scenario, multitarget, and multi-interest, more and more recent w…
LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Bohao Wang, Feng Liu, Changwang Zhang +8
Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy i…
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Taotian Pang, Xingyu Lou, Fei Zhao +5
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. How…