RecSys2020推荐系统论文集锦

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嘿，记得给“机器学习与推荐算法”添加星标

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。

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.

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.

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.

更多接收的论文可以访问： https://dblp.org/db/conf/recsys/recsys2020.html