activity
20202026
most citedKnowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs

8 citations · 18 across the 13 of their papers we have counts for

collaborators

14 papers

cs.IR2026

Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation

Yanan Cao, Anay Dombe, Murali Mohana Krishna Dandu +5

Next-basket repurchase recommendation is commonly formulated as a ranking task: given a customer's purchase history, the system ranks previously purchased items that may be needed…

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.CV2026

Attribute-Conditioned Multimodal Slot Factorization for Controllable Fashion Retrieval

Najmeh Forouzandehmehr, Topojoy Biswas, Evren Korpeoglu +1

Fashion retrieval often requires satisfying multiple attributes at once, such as category, color, pattern, and demographic. Monolithic embeddings mix these signals into a single ve…

cs.IR2026

CASE: Cadence-Aware Set Encoding for Large-Scale Next Basket Repurchase Recommendation

Yanan Cao, Ashish Ranjan, Sinduja Subramaniam +3

Repurchase behavior is a primary signal in large-scale retail recommendation, particularly in categories with frequent replenishment: many items in a user's next basket were previo…

cs.IR2026

Campaign-2-PT-RAG: LLM-Guided Semantic Product Type Attribution for Scalable Campaign Ranking

Yiming Che, Mansi Ranjit Mane, Keerthi Gopalakrishnan +8

E-commerce campaign ranking models require large-scale training labels indicating which users purchased due to campaign influence. However, generating these labels is challenging b…

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…