collaborators

6 papers

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…

cs.IR2025

Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context

Akshay Kekuda, Murali Mohana Krishna Dandu, Rimita Lahiri +4

Modern e-commerce platforms strive to enhance customer experience by providing timely and contextually relevant recommendations. However, recommending general merchandise to custom…

cs.CV2025

Spatial Reasoning in Foundation Models: Benchmarking Object-Centric Spatial Understanding

Vahid Mirjalili, Ramin Giahi, Sriram Kollipara +9

Spatial understanding is a critical capability for vision foundation models. While recent advances in large vision models or vision-language models (VLMs) have expanded recognition…

cs.LG2025

S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipe

Yanan Cao, Omid Memarrast, Shiqin Cai +3

In grocery e-commerce, customers often build ingredient baskets guided by dietary preferences but lack the expertise to create complete meals. Leveraging recipe knowledge to recomm…