2 citations · 8 across the 25 of their papers we have counts for
9 papers · 1 filter
Empowering Denoising Sequential Recommendation with Large Language Model Embeddings
Tongzhou Wu, Yuhao Wang, Maolin Wang +2
Sequential recommendation aims to capture user preferences by modeling sequential patterns in user-item interactions. However, these models are often influenced by noise such as ac…
SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation
Binhao Wang, Yutian Xiao, Maolin Wang +4
Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry's inadequacy for complex relational structures, critica…
Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs
Yuhao Wang, Junwei Pan, Xinhang Li +6
Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions. Recently, the powerful capabilities of large…
Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation
Yi Wen, Yue Liu, Derong Xu +9
Multi-Domain Recommendation (MDR) achieves the desirable recommendation performance by effectively utilizing the transfer information across different domains. Despite the great su…
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation
Maolin Wang, Sheng Zhang, Ruocheng Guo +6
Recent deep sequential recommendation models often struggle to effectively model key characteristics of user behaviors, particularly in handling sequence length variations and capt…
Scalable Dynamic Embedding Size Search for Streaming Recommendation
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-w…
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
Sheng Zhang, Maolin Wang, Wanyu Wang +6
Transformer-based models have gained significant traction in sequential recommender systems (SRSs) for their ability to capture user-item interactions effectively. However, these m…
The 2nd Workshop on Recommendation with Generative Models
Wenjie Wang, Yang Zhang, Xinyu Lin +7
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This works…
DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems
Sheng Zhang, Maolin Wang, Yao Zhao +6
In the era of data proliferation, efficiently sifting through vast information to extract meaningful insights has become increasingly crucial. This paper addresses the computationa…