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20212026
most citedKnowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs

8 citations · 27 across the 38 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

Timing-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation

Akshay Kekuda, Shreeranjani Srirangamsridharan, Ishan Bhatt +5

Repurchase recommenders in e-commerce are commonly framed as a binary question asking "will this customer buy this item within W days", a formulation that requires a separately tra…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

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…

cs.AI2025

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…