Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua
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引用次数: 183

Abstract

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users' online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation"Action" Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits.
评估-行动-反思:对话系统和推荐系统之间的深度互动
推荐系统正在采用会话技术来动态获取用户偏好,并克服其静态模型的固有局限性。一个成功的会话推荐系统(CRS)需要正确处理会话和推荐之间的交互。我们认为需要解决三个基本问题:1)关于商品属性该问什么问题,2)何时推荐商品,以及3)如何适应用户的在线反馈。据我们所知,目前还缺乏解决这些问题的统一框架。在这项工作中,我们通过提出一个新的CRS框架来填补这个缺失的交互框架的空白,该框架名为评估“行动”反射,或EAR,它由三个阶段组成,以更好地与用户交谈。(1)估计,构建预测模型来估计用户对物品和物品属性的偏好;(2) Action,学习对话策略,根据estimate阶段和对话历史来决定是询问属性还是推荐项目;(3) Reflection,当用户拒绝Action阶段的推荐时,会更新推荐模型。我们提出了关于二进制和枚举问题的两个对话场景,并分别在来自Yelp和LastFM的两个数据集上对每个场景进行了广泛的实验。我们的实验表明,与最先进的CRM方法相比,该方法有了显著的改进[32],对应于更少的会话次数和更高的推荐点击率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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