8 citations · 27 across the 38 of their papers we have counts for
23 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…
CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation
Zezhong Fan, Ziheng Chen, Luyi Ma +5
Generative recommendation (GeneRec) has introduced a new paradigm that represents items as discrete semantic tokens and predicts items in a generative manner. Despite its strong pe…
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…
Latent Customer Segmentation and Value-Based Recommendation Leveraging a Two-Stage Model with Missing Labels
Keerthi Gopalakrishnan, Tianning Dong, Chia-Yen Ho +5
The success of businesses depends on their ability to convert consumers into loyal customers. A customer's value proposition is a primary determinant in this process, requiring a b…
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…
Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data
Lalitesh Morishetti, Abhay Kumar, Jonathan Scott +6
In this paper, we present a novel model architecture for optimizing personalized product search ranking using a multi-task learning (MTL) framework. Our approach uniquely integrate…
CARTS: Collaborative Agents for Recommendation Textual Summarization
Jiao Chen, Kehui Yao, Reza Yousefi Maragheh +6
Current recommendation systems often require some form of textual data summarization, such as generating concise and coherent titles for product carousels or other grouped item dis…
CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
Najmeh Forouzandehmehr, Reza Yousefi Maragheh, Sriram Kollipara +4
Automated content-aware layout generation -- the task of arranging visual elements such as text, logos, and underlays on a background canvas -- remains a fundamental yet under-expl…
Improving Sequential Recommender Systems with Online and In-store User Behavior
Luyi Ma, Aashika Padmanabhan, Anjana Ganesh +9
Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shoppin…
Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations
Luyi Ma, Xiaohan Li, Zezhong Fan +7
Integrating diverse data modalities is crucial for enhancing the performance of personalized recommendation systems. Traditional models, which often rely on singular data sources,…
Decoding Style: Efficient Fine-Tuning of LLMs for Image-Guided Outfit Recommendation with Preference
Najmeh Forouzandehmehr, Nima Farrokhsiar, Ramin Giahi +2
Personalized outfit recommendation remains a complex challenge, demanding both fashion compatibility understanding and trend awareness. This paper presents a novel framework that h…
Leveraging User-Generated Reviews for Recommender Systems with Dynamic Headers
Shanu Vashishtha, Abhay Kumar, Lalitesh Morishetti +2
E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests. Most of these platforms assist their customers in the shopping process by offerin…
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…
Event-based Product Carousel Recommendation with Query-Click Graph
Luyi Ma, Nimesh Sinha, Parth Vajge +3
Many current recommender systems mainly focus on the product-to-product recommendations and user-to-product recommendations even during the time of events rather than modeling the…
Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs
Najmeh Forouzandehmehr, Yijie Cao, Nikhil Thakurdesai +6
The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or s…
LLM-TAKE: Theme Aware Keyword Extraction Using Large Language Models
Reza Yousefi Maragheh, Chenhao Fang, Charan Chand Irugu +8
Keyword extraction is one of the core tasks in natural language processing. Classic extraction models are notorious for having a short attention span which make it hard for them to…
GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation
Ramin Giahi, Reza Yousefi Maragheh, Nima Farrokhsiar +5
Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products. Despi…
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…
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Xiaohan Li, Zheng Liu, Luyi Ma +4
Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequ…
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…
NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation
Luyi Ma, Jianpeng Xu, Jason H. D. Cho +3
The complementary item recommender system (CIRS) recommends the complementary items for a given query item. Existing CIRS models consider the item co-purchase signal as a proxy of…
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…