RSPapers | 对话推荐系统论文合集

随着大数据时代的飞速发展，信息逐渐呈现出过载状态。推荐系统，作为近年来实现信息生产者与消费者之间利益均衡化的有效手段之一，越来越发挥着举足轻重的作用。像今日头条、抖音这样的APP之所以如此之火，让人们欲罢不能，无非是抓住了用户想看什么的心理，那么如何才能抓住用户的心理，那就需要推荐系统的帮助了。因此在这个张扬个性的时代，无论你是开发工程师还是产品经理，我们都有必要了解一下个性化推荐的一些经典工作与前沿动态。于是我们于2018年创建了Github项目RSPapers：

https://github.com/hongleizhang/RSPapers

该项目提供了一些关于推荐系统的经典综述文章、主流的推荐算法文章、社会化推荐算法论文、基于深度学习的推荐系统论文（包括目前较火的GCN网络）以及关于专门处理冷启动问题的相关论文、推荐中的效率问题以及推荐当中的探索与利用问题、推荐可解释性、基于评论的推荐等。当然该项目包含但不局限于以上这些模块。目前累计star数量已达2.4k，感谢大家的贡献与支持。

最近对话推荐系统（Conversational RS，CRS）由于其可以与用户进行多轮交互进而提供更好的推荐体验，在学术界和工业界中都得到了非常多的关注。我们注意到今年的SIGIR会议中专门开设了关于CRS的Tutorial，直播/回看地址为：

https://sigir-schedule.baai.ac.cn/live/DAY26?tuid=tut0003

可见关于其研究逐渐火热，另外尤其是2020年发表了许多关于CRS的论文，特此进行了整理，希望对大家有所帮助。

Conversational RS

Dietmar et al. A Survey on Conversational Recommender Systems. arXiv, 2020.
Zhao et al. Interactive collaborative filtering. CIKM, 2013.
Negar et al. Context adaptation in interactive recommender systems. RecSys, 2014.
Yasser et al. History-guided conversational recommendation. WWW, 2014.
Konstantina et al. Towards Conversational Recommender Systems. KDD, 2016.
Konstantina et al. Q&R: A Two-Stage Approach toward Interactive Recommendation. KDD, 2018.
Sun et al. Conversational Recommender System. SIGIR, 2018.
Yongfeng et al. Towards Conversational Search and Recommendation: System Ask, User Respond. CIKM, 2018.
Raymond et al. Towards Deep Conversational Recommendations. NeurIPS, 2018.
Tong et al. A Visual Dialog Augmented Interactive Recommender System. KDD, 2019.
Qibin et al. Towards Knowledge-Based Recommender Dialog System. EMNLP, 2019.
Yuanjiang et al. Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems. SIGIR, 2020.
Wenqiang et al. Conversational Recommendation: Formulation, Methods, and Evaluation. SIGIR, 2020.
Xingshan et al. Dynamic Online Conversation Recommendation. ACL, 2020.
Wenqiang et al. Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. WSDM, 2020.
Kun et al. Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion. KDD, 2020.
Wenqiang et al. Interactive Path Reasoning on Graph for Conversational Recommendation. KDD, 2020.
Sijin et al. Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning. SIGIR, 2020.
Kai et al. Latent Linear Critiquing for Conversational Recommender Systems. WWW, 2020.
Lixin et al. Neural Interactive Collaborative Filtering. SIGIR, 2020.
Lixin et al. Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation. WSDM, 2020.
Shijun et al. Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users. arXiv, 2020.
Zeming et al. Towards Conversational Recommendation over Multi-Type Dialogs. ACL, 2020.
Zhongxia et al. Towards Explainable Conversational Recommendation. IJCAI, 2020.
Jie et al. Towards Question-based Recommender Systems. SIGIR, 2020.
Hu et al. User Memory Reasoning for Conversational Recommendation. arXiv, 2020.
Kai et al. Latent Linear Critiquing for Conversational Recommender Systems. WWW, 2020.

Conversational RS

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