11 citations · 33 across the 20 of their papers we have counts for
21 papers
Difficulty-Aware Semantic-ID Optimization for Generative Recommendation
Xin Yu, Stephen Li, Sina Aghaei +6
Semantic-ID-based generative recommendation casts retrieval and ranking as autoregressive generation over hierarchical item identifiers. A common recipe is SFT followed by GRPO, ye…
Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models
Jiachen Jiang, Jinxin Zhou, Bo Peng +2
Achieving better alignment between vision embeddings and Large Language Models (LLMs) is crucial for enhancing the abilities of Multimodal LLMs (MLLMs), particularly for recent mod…
Planning with Diffusion Models for Target-Oriented Dialogue Systems
Hanwen Du, Bo Peng, Xia Ning
Target-Oriented Dialogue (TOD) remains a significant challenge in the LLM era, where strategic dialogue planning is crucial for directing conversations toward specific targets. How…
Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance
Ziqi Chen, Bo Peng, Tianhua Zhai +2
Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSM…
log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling
Xiao Hu, Ziqi Chen, Bo Peng +2
Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of arti…
Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data
Xinyi Ling, Hanwen Du, Bo Peng +2
Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention from the research community…
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search
Hanwen Du, Bo Peng, Xia Ning
Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods tra…
SWaT: Statistical Modeling of Video Watch Time through User Behavior Analysis
Shentao Yang, Haichuan Yang, Linna Du +5
The significance of estimating video watch time has been highlighted by the rising importance of (short) video recommendation, which has become a core product of mainstream social…
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Bo Peng, Xinyi Ling, Ziru Chen +2
With tremendous efforts on developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfac…
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation
Bo Peng, Ziqi Chen, Srinivasan Parthasarathy +1
Self-attention (SA) mechanisms have been widely used in developing sequential recommendation (SR) methods, and demonstrated state-of-the-art performance. However, in this paper, we…
Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing
Shunian Xiang, Patrick J. Lawrence, Bo Peng +4
Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have…
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation
Bo Peng, Ben Burns, Ziqi Chen +2
In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhance…
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation
Bo Peng, Srinivasan Parthasarathy, Xia Ning
Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-ba…
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ziqi Chen, Bo Peng, Srinivasan Parthasarathy +1
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules. In this paper, we formulated an in silico shape-conditioned molecule…
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation
Bo Peng, Srinivasan Parthasarathy, Xia Ning
Sequential recommendation aims to recommend the next item of users' interest based on their historical interactions. Recently, the self-attention mechanism has been adapted for seq…
Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation
Bo Peng, Chang-Yu Tai, Srinivasan Parthasarathy +1
Session-based recommendation aims to generate recommendations for the next item of users' interest based on a given session. In this manuscript, we develop prospective preference e…
Hybrid Collaborative Filtering Models for Clinical Search Recommendation
Zhiyun Ren, Bo Peng, Titus K. Schleyer +1
With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large am…
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy +1
Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed mo…
HAM: Hybrid Associations Models for Sequential Recommendation
Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy +1
Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectorie…
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data
Bo Peng, Xiaohui Yao, Shannon L. Risacher +3
Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and ti…
CNN-based Dual-Chain Models for Knowledge Graph Learning
Bo Peng, Renqiang Min, Xia Ning
Knowledge graph learning plays a critical role in integrating domain specific knowledge bases when deploying machine learning and data mining models in practice. Existing methods o…