12月1日论文推荐（附下载地址）
论文名：
Q&R: A Two-Stage Approach toward Interactive Recommendation
作者：
Konstantina Christakopoulou (University of Minnesota), Alex Beutel (Google Inc), Rui Li (Google Inc), Sagar Jain (Google Inc), Ed H. Chi (Google Inc)
推荐理由：
本文主要探讨交互式推荐问题，提出一个两阶段的交互推荐方法。传统的推荐一般都是首先分析用户兴趣，然后基于用户兴趣和推荐信息之间的匹配度进行推荐，然而这种方法很难精准捕获用户的实时兴趣以及用户查询场景的真实意图，交互式推荐是近年业界备受关注的问题。本质上这是一个Active Learning（主动学习）或者Online Learning（在线学习）问题。该文提出一个基于RNN的问题（话题）自动生成方法（第一阶段），也就是系统会根据用户兴趣自动生成话题，主动询问用户，然后在第二阶段根据用户反馈修正推荐结果，最后该方法在YouTube上进行了验证。基于深度学习的话题生成可以大大提高最终的推荐结果。
Abstract
Recommendation systems, prevalent in many applications, aim to surface to users the right content at the right time. Recently, researchers have aspired to develop conversational systems that offer seamless interactions with users, more effectively eliciting user preferences and offering better recommendations. Taking a step towards this goal, this paper explores the two stages of a single round of conversation with a user: which question to ask the user, and how to use their feedback to respond with a more accurate recommendation. Following these two stages, first, we detail an RNN-based model for generating topics a user might be interested in, and then extend a state-of-the-art RNN-based video recommender to incorporate the user’s selected topic. We describe our proposed system Q&R, i.e., Question & Recommendation, and the surrogate tasks we utilize to bootstrap data for training our models. We evaluate different components of Q&R on live traffic in various applications within YouTube: User Onboarding, Homepage Recommendation, and Notifications. Our results demonstrate that our approach improves upon state-of-the-art recommendation models, including RNNs, and makes these applications more useful, such as a > 1% increase in video notifications opened. Further, our design choices can be useful to practitioners wanting to transition to more conversational recommendation systems.
其中下图1是系统主动提问的界面；下图2是文章使用的基于RNN的话题自动生成模型（当然第二阶段还有一个混合模型）；下图3是针对自动生成话题的评估结果。
图1：系统主动提问界面
图2：话题自动生成模型RNN
图3：提问话题评估结果
论文下载链接
http://alexbeutel.com/papers/q-and-r-kdd2018.pdf