24 citations · 38 across the 6 of their papers we have counts for
4 papers · 1 filter
Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
Zhendong Chu, Nan Wang, Hongning Wang
Conversational Recommender Systems (CRS) actively elicit user preferences to generate adaptive recommendations. Mainstream reinforcement learning-based CRS solutions heavily rely o…
Explanation as a Defense of Recommendation
Aobo Yang, Nan Wang, Hongbo Deng +1
Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanatio…
Directional Multivariate Ranking
Nan Wang, Hongning Wang
User-provided multi-aspect evaluations manifest users' detailed feedback on the recommended items and enable fine-grained understanding of their preferences. Extensive studies have…
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees
Yiyi Tao, Yiling Jia, Nan Wang +1
Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to…