5 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Causal Structure Learning with Recommendation System
Shuyuan Xu, Da Xu, Evren Korpeoglu +4
A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by us…
GAN-based Recommendation with Positive-Unlabeled Sampling
Yao Zhou, Jianpeng Xu, Jun Wu +4
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products. In this work, we propose a Generative Advers…