8 citations · 13 across the 14 of their papers we have counts for
15 papers
LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks
Luyi Ma, Wanjia Sherry Zhang, Zezhong Fan +10
On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training. In this work, we propose LL…
CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation
Zezhong Fan, Ziheng Chen, Luyi Ma +5
Generative recommendation (GeneRec) has introduced a new paradigm that represents items as discrete semantic tokens and predicts items in a generative manner. Despite its strong pe…
Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction
Yanan Cao, Farnaz Fallahi, Murali Mohana Krishna Dandu +9
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from…
To See or To Read: User Behavior Reasoning in Multimodal LLMs
Tianning Dong, Luyi Ma, Varun Vasudevan +3
Multimodal Large Language Models (MLLMs) are reshaping how modern agentic systems reason over sequential user-behavior data. However, whether textual or image representations of us…
No-Human in the Loop: Agentic Evaluation at Scale for Recommendation
Tao Zhang, Kehui Yao, Luyi Ma +7
Evaluating large language models (LLMs) as judges is increasingly critical for building scalable and trustworthy evaluation pipelines. We present ScalingEval, a large-scale benchma…
LayoutAgent: A Vision-Language Agent Guided Compositional Diffusion for Spatial Layout Planning
Zezhong Fan, Xiaohan Li, Luyi Ma +6
Designing realistic multi-object scenes requires not only generating images, but also planning spatial layouts that respect semantic relations and physical plausibility. On one han…
GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization
Luyi Ma, Wanjia Zhang, Kai Zhao +15
Generative models have recently demonstrated strong potential in multi-behavior recommendation systems, leveraging the expressive power of transformers and tokenization to generate…
Improving Sequential Recommender Systems with Online and In-store User Behavior
Luyi Ma, Aashika Padmanabhan, Anjana Ganesh +9
Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shoppin…
Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations
Luyi Ma, Xiaohan Li, Zezhong Fan +7
Integrating diverse data modalities is crucial for enhancing the performance of personalized recommendation systems. Traditional models, which often rely on singular data sources,…
Event-based Product Carousel Recommendation with Query-Click Graph
Luyi Ma, Nimesh Sinha, Parth Vajge +3
Many current recommender systems mainly focus on the product-to-product recommendations and user-to-product recommendations even during the time of events rather than modeling the…
Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs
Najmeh Forouzandehmehr, Yijie Cao, Nikhil Thakurdesai +6
The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or s…
LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processing
Luyi Ma, Nikhil Thakurdesai, Jiao Chen +4
Data processing is one of the fundamental steps in machine learning pipelines to ensure data quality. Majority of the applications consider the user-defined function (UDF) design p…
Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs
Jiao Chen, Luyi Ma, Xiaohan Li +7
Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complement…
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Xiaohan Li, Zheng Liu, Luyi Ma +4
Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequ…
NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation
Luyi Ma, Jianpeng Xu, Jason H. D. Cho +3
The complementary item recommender system (CIRS) recommends the complementary items for a given query item. Existing CIRS models consider the item co-purchase signal as a proxy of…