12 citations · 13 across the 5 of their papers we have counts for
5 papers · 1 filter
Representation Quantization for Collaborative Filtering Augmentation
Yunze Luo, Yinjie Jiang, Gaode Chen +9
As the core algorithm in recommendation systems, collaborative filtering (CF) algorithms inevitably face the problem of data sparsity. Since CF captures similar users and items for…
Towards Comprehensible Recommendation with Large Language Model Fine-tuning
Yunze Luo, Yinjie Jiang, Gaode Chen +2
Recommender systems have become increasingly ubiquitous in daily life. While traditional recommendation approaches primarily rely on ID-based representations or item-side content f…
Prompt Tuning for Item Cold-start Recommendation
Yuezihan Jiang, Gaode Chen, Wenhan Zhang +6
The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt lear…
Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning
Yunze Luo, Yuezihan Jiang, Yinjie Jiang +5
With the rise of e-commerce and short videos, online recommender systems that can capture users' interests and update new items in real-time play an increasingly important role. In…
A Unified Framework for Cross-Domain Recommendation
Jiangxia Cao, Shen Wang, Gaode Chen +4
In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising method…