RecSys2020推荐系统论文集锦

嘿，记得给“机器学习与推荐算法”添加星标

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第14届推荐人自己的年会RecSys已在9月22日到26日在线上举行。大会围绕着推荐系统相关问题进行了3场KeyNotes，5场Tutorials，接收了41篇长文，26篇短文。

通过对主题演讲以及教程的总结发现，此次大会主要聚焦在了推荐系统中的Bias问题以及对话推荐系统、对抗机器学习在推荐中的应用等。

主题演讲为以下3个：

• 4 Reasons Why Social Media Make Us Vulnerable to Manipulation.

  by Filippo Menczer.

• Bias in Search and Recommender Systems.

  by Ricardo Baeza-Yates.

• "You Really Get Me": Conversational AI Agents That Can Truly Understand and Help Users. 

  by Michelle Zhou.

大会教程为以下6个：

• Adversarial Learning for Recommendation: Applications for Security and Generative Tasks - Concept to Code.

  by Vito Walter Anelli et al.

• Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation.

  by David Rohde et al.

• Counteracting Bias and Increasing Fairness in Search and Recommender Systems. 

  by Ruoyuan Gao et al.

• Introduction to Bandits in Recommender Systems.

  by Andrea Barraza-Urbina et al.

• Tutorial on Conversational Recommendation Systems.

  by Zuohui Fu et al.

• Tutorial: Feature Engineering for Recommender Systems.

  by Benedikt Schifferer et al.

另外，大会揭晓了今年的最佳论文奖、最佳论文提名奖、最佳短文奖。具体标题及单位如下：

• Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
  by H. Tang, J. Liu, M. Zhao, X. Gong (Best Long Paper)

• Exploiting Performance Estimates for Augmenting Recommendation Ensembles
  by  G. Penha, R. L. T. Santos  ( Best Long Paper Runner-up )
• ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation
  by  F. Mi, X. Lin, B. Faltings  ( Best Short Paper )

最后，小编为大家收集整理了部分相关主题的论文。其中对论文的总结发现，除了以下列出的大类外，还有一些非常有意思的工作，比如对可复现性和公平对比的思考、多智能体强化学习与推荐系统的结合、对矩阵分解和神经协同过滤方法的思考等等。

一. 序列推荐

• From the lab to production: A case study of session-based recommendations in the home-improvement domain.

• ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation.

• Exploring Longitudinal Effects of Session-based Recommendations.

• Long-tail Session-based Recommendation.

• Context-aware Graph Embedding for Session-based News Recommendation.

• Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming Sessions.

二. 可解释性推荐

• Explainable Recommendation for Repeat Consumption.

• Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering.

• Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners.

三. 无偏的和公平的推荐

• Bias in Search and Recommender Systems

• Debiasing Item-to-Item Recommendations With Small Annotated Datasets.

• Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems.

• Unbiased Ad Click Prediction for Position-aware Advertising Systems.

• Unbiased Learning for the Causal Effect of Recommendation.

• Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning.

• The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation.

• Counteracting Bias and Increasing Fairness in Search and Recommender Systems.

• Counteracting Bias and Increasing Fairness in Search and Recommender Systems.

• The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation.

• Fairness-aware Recommendation with librec-auto.

• Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance.

回复"RecSys2020"获取合集，更多接收的论文可以访问： https://dblp.org/db/conf/recsys/recsys2020.html

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