:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Vijayaraghavan, Sairamvinay, Mohapatra, Prasant
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval Machine Learning
Online Access:	https://arxiv.org/abs/2405.01855
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stability of Explainable Recommendation
by: Vijayaraghavan, Sairamvinay, et al.
Published: (2024)

Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph
by: Wang, Menghan, et al.
Published: (2024)

Efficient and Robust Regularized Federated Recommendation
by: Liu, Langming, et al.
Published: (2024)

Review of Explainable Graph-Based Recommender Systems
by: Markchom, Thanet, et al.
Published: (2024)

Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
by: Liu, Siliang, et al.
Published: (2024)

RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors
by: Cai, Miaomiao, et al.
Published: (2026)

Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph
by: Balloccu, Giacomo, et al.
Published: (2023)

Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems
by: Kolodner, Matthew, et al.
Published: (2024)

Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
by: Tan, Juntao, et al.
Published: (2023)

MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
by: Yang, Ching-Wen, et al.
Published: (2024)

Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations
by: Kaur, Kirandeep, et al.
Published: (2025)

Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
by: Zhong, Shanshan, et al.
Published: (2024)

Uncertainty-Aware Explainable Recommendation with Large Language Models
by: Peng, Yicui, et al.
Published: (2024)

Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
by: Shimizu, Ryotaro, et al.
Published: (2024)

PolyRecommender: A Multimodal Recommendation System for Polymer Discovery
by: Wang, Xin, et al.
Published: (2025)

Robust Reinforcement Learning Objectives for Sequential Recommender Systems
by: Mozifian, Melissa, et al.
Published: (2023)

Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability
by: Paz-Ruza, Jorge, et al.
Published: (2023)

LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation
by: Zhao, Hongke, et al.
Published: (2024)

Graph Augmentation for Recommendation
by: Zhang, Qianru, et al.
Published: (2024)

Continual Recommender Systems
by: Yoo, Hyunsik, et al.
Published: (2025)

When and What to Recommend: Joint Modeling of Timing and Content for Active Sequential Recommendation
by: Chai, Jin, et al.
Published: (2025)

A Design Space for Explainable Ranking and Ranking Models
by: Hazwani, I. Al, et al.
Published: (2022)

Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty
by: Li, Jiayu, et al.
Published: (2025)

Debiased Recommendation with Noisy Feedback
by: Li, Haoxuan, et al.
Published: (2024)

Supplier Recommendation in Online Procurement
by: Coscrato, Victor, et al.
Published: (2024)

Diffusion Cross-domain Recommendation
by: Xuan, Yuner
Published: (2024)

Masked Diffusion for Generative Recommendation
by: Shah, Kulin, et al.
Published: (2025)

Semantic IDs for Music Recommendation
by: Mei, M. Jeffrey, et al.
Published: (2025)

Calibrated Recommendations with Contextual Bandits
by: Feijer, Diego, et al.
Published: (2025)

Plausible Counterfactual Explanations of Recommendations
by: Černý, Jakub, et al.
Published: (2025)

Online Learning for Recommendations at Grubhub
by: Egg, Alex
Published: (2021)

Dataset-Agnostic Recommender Systems
by: Wijaya, Tri Kurniawan, et al.
Published: (2025)

System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes
by: Agarwal, Arpit, et al.
Published: (2024)

Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings
by: Pang, Linsey, et al.
Published: (2024)

Explainable identification of similarities between entities for discovery in large text
by: Joshi, Akhil, et al.
Published: (2025)

Unifying Inductive, Cross-Domain, and Multimodal Learning for Robust and Generalizable Recommendation
by: Chung, Chanyoung, et al.
Published: (2025)

Calibrating the Predictions for Top-N Recommendations
by: Sato, Masahiro
Published: (2024)

Towards Scalable Semantic Representation for Recommendation
by: Zhang, Taolin, et al.
Published: (2024)

Debiased Model-based Interactive Recommendation
by: Li, Zijian, et al.
Published: (2024)

Co-clustering for Federated Recommender System
by: He, Xinrui, et al.
Published: (2024)