24 citations · 63 across the 8 of their papers we have counts for
8 papers
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…
CORe: Capitalizing On Rewards in Bandit Exploration
Nan Wang, Branislav Kveton, Maryam Karimzadehgan
We propose a bandit algorithm that explores purely by randomizing its past observations. In particular, the sufficient optimism in the mean reward estimates is achieved by exploiti…
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…
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network
Xiatian Zhang, Xunshi He, Nan Wang +1
For high-dimensional data, there are huge communication costs for distributed GBDT because the communication volume of GBDT is related to the number of features. To overcome this p…
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…
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
Nan Wang, Zhen Qin, Xuanhui Wang +1
Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback. Though theoretically sound in correcting the bi…
BPMR: Bayesian Probabilistic Multivariate Ranking
Nan Wang, Hongning Wang
Multi-aspect user preferences are attracting wider attention in recommender systems, as they enable more detailed understanding of users' evaluations of items. Previous studies sho…
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…