Recommendation System
Recommendation systems aim to predict user preferences and provide personalized suggestions, primarily focusing on improving accuracy, diversity, and efficiency. Current research emphasizes incorporating diverse data sources (text, images, location, user interactions across platforms) into sophisticated models, including transformer networks, graph neural networks, and large language models, often within federated learning frameworks to address privacy concerns. These advancements are crucial for enhancing user experience across various applications (e-commerce, social media, search engines) and for developing more robust, explainable, and bias-mitigated systems.
Papers
STAR: A Simple Training-free Approach for Recommendations using Large Language Models
Dong-Ho Lee, Adam Kraft, Long Jin, Nikhil Mehta, Taibai Xu, Lichan Hong, Ed H. Chi, Xinyang Yi
LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Model
Vansh Kharidia, Dhruvi Paprunia, Prashasti Kanikar
A Parameter Update Balancing Algorithm for Multi-task Ranking Models in Recommendation Systems
Jun Yuan, Guohao Cai, Zhenhua Dong
CUPID: A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform
Beomsu Kim, Sangbum Kim, Minchan Kim, Joonyoung Yi, Sungjoo Ha, Suhyun Lee, Youngsoo Lee, Gihun Yeom, Buru Chang, Gihun Lee
Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis
Michaël Soumm, Alexandre Fournier-Montgieux, Adrian Popescu, Bertrand Delezoide
Multi-modal clothing recommendation model based on large model and VAE enhancement
Bingjie Huang, Qingyi Lu, Shuaishuai Huang, Xue-she Wang, Haowei Yang
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
Qianru Zhang, Peng Yang, Junliang Yu, Haixin Wang, Xingwei He, Siu-Ming Yiu, Hongzhi Yin