论文标题
极简主义和高性能的对话推荐和用户偏好的不确定性估计
Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference
论文作者
论文摘要
会话推荐系统（CRS）正在成为一种用户友好的方式，可以捕获用户对候选项目和属性的动态偏好。 Multi-Shot CRS旨在多次提出建议，直到用户接受建议或在耐心结束时离开。现有的作品是通过增强学习（RL）培训的，这可能会遭受不稳定的学习和对计算的高度需求。在这项工作中，我们提出了一个简单有效的CRS，极简主义的非增强交互式对话推荐网络（Minocorn）。微米人对估计的用户偏好的认知不确定性进行了建模，并查询用户对具有最高不确定性的属性。该系统采用简单的网络体系结构，并使用单个规则做出查询VS-启用决策。有些令人惊讶的是，这种极简主义方法的表现优于三个现实世界数据集的最先进的RL方法。我们希望微米将成为未来研究的宝贵基准。
Conversational recommendation system (CRS) is emerging as a user-friendly way to capture users' dynamic preferences over candidate items and attributes. Multi-shot CRS is designed to make recommendations multiple times until the user either accepts the recommendation or leaves at the end of their patience. Existing works are trained with reinforcement learning (RL), which may suffer from unstable learning and prohibitively high demands for computing. In this work, we propose a simple and efficient CRS, MInimalist Non-reinforced Interactive COnversational Recommender Network (MINICORN). MINICORN models the epistemic uncertainty of the estimated user preference and queries the user for the attribute with the highest uncertainty. The system employs a simple network architecture and makes the query-vs-recommendation decision using a single rule. Somewhat surprisingly, this minimalist approach outperforms state-of-the-art RL methods on three real-world datasets by large margins. We hope that MINICORN will serve as a valuable baseline for future research.
