8 citations · 18 across the 13 of their papers we have counts for
8 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…
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction
Chenhao Fang, Xiaohan Li, Zezhong Fan +5
Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry. The provision of precise product attribute…
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…
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…
Towards the D-Optimal Online Experiment Design for Recommender Selection
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluat…
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…