15 citations · 30 across the 8 of their papers we have counts for
8 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…
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
Xinrui He, Mengting Ai, Junting Wang +2
Clinical care often relies on key laboratory indicators, yet real-world patient visits are sparse and tests are ordered irregularly, leading to pervasive missingness. While many im…
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…
Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems
Junting Wang, Adit Krishnan, Hari Sundaram +1
Modern neural collaborative filtering techniques are critical to the success of e-commerce, social media, and content-sharing platforms. However, despite technical advances -- for…
Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework
Aravind Sankar, Junting Wang, Adit Krishnan +1
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs. We overcome two key limitations of message passing in popular graph neural networks…
An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms
Kanika Narang, Chaoqi Yang, Adit Krishnan +3
This paper addresses the question of identifying the best candidate answer to a question on Community Question Answer (CQA) forums. The problem is important because Individuals oft…