1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.IR2026
SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
Jiangnan Xia, Xuansheng Wu, Yu Yang +2
Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors. Most ex…
cs.IR2024★ 1 cited
Uncertainty-Aware Explainable Recommendation with Large Language Models
Yicui Peng, Hao Chen, Chingsheng Lin +7
Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tai…