9 citations · 13 across the 4 of their papers we have counts for
4 papers
Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation
Junting Wang, Xinrui He, Yunzhe Li +1
Semantic IDs (SIDs) are now a central component of generative recommendation. Current SID-based systems assign three roles to the same token sequence. Shared prefixes are intended…
Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items
Junting Wang, Chenghuan Guo, Jiao Yang +3
We study the problem of inferring substitutable and complementary items, which underpins applications such as alternative and follow-up purchase suggestions. Existing approaches ty…
LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation
Yunzhe Li, Junting Wang, Hari Sundaram +1
Zero-shot cross-domain sequential recommendation (ZCDSR) enables predictions in unseen domains without additional training or fine-tuning, addressing the limitations of traditional…
A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot Transfer
Junting Wang, Praneet Rathi, Hari Sundaram
Sequential recommenders are crucial to the success of online applications, \eg e-commerce, video streaming, and social media. While model architectures continue to improve, for eve…