RecSys2020-推荐系统顶会论文大集锦

Posted 2021-05-01 推荐系统与计算广告

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[1]。

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主题演讲

1. 社交媒体让我们容易被操纵的4个原因

4 Reasons Why Social Media Make Us Vulnerable to Manipulation

https://dl.acm.org/doi/pdf/10.1145/3383313.3418434

2. 搜索和推荐中的偏差

Bias in Search and Recommender Systems

https://dl.acm.org/doi/pdf/10.1145/3383313.3418435

3. “你真懂我的意思”: 能够真正理解并帮助用户的对话型人工智能代理

“You Really Get Me”: Conversational AI Agents That Can Truly Understand and Help Users

https://dl.acm.org/doi/pdf/10.1145/3383313.3418436

推荐Tutorial

1. 推荐的对抗性学习: 安全和生成任务的应用。

Adversarial Learning for Recommendation: Applications for Security and Generative Tasks — Concept to Code

https://dl.acm.org/doi/abs/10.1145/3383313.3411447

2. 基于贝叶斯值的推荐:一个基于模型的替代代理和基于反事实策略的推荐。

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

https://dl.acm.org/doi/pdf/10.1145/3383313.3411544

3. 在搜索和推荐系统中消除偏见和增加公平性

Counteracting Bias and Increasing Fairness in Search and Recommender Systems

https://dl.acm.org/doi/pdf/10.1145/3383313.3411545

4. 推荐系统中的Bandits介绍

Introduction to Bandits in Recommender Systems.

https://dl.acm.org/doi/pdf/10.1145/3383313.3411547

5. 基于会话的推荐系统教程

Tutorial on Conversational Recommendation Systems

https://dl.acm.org/doi/pdf/10.1145/3383313.3411548

6. 推荐系统的特性工程教程

Tutorial: Feature Engineering for Recommender Systems

https://dl.acm.org/doi/abs/10.1145/3383313.3411543

最佳论文奖、最佳论文提名奖、最佳短文奖

(Best Long Paper)

渐进式分层提取(PLE): 一种用于个性化推荐的新型多任务学习(MTL)模型

Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations

https://dl.acm.org/doi/abs/10.1145/3383313.3412236

(Best Long Paper Runner-up)

利用性能估计来增强推荐集成

Exploiting Performance Estimates for Augmenting Recommendation Ensembles

https://dl.acm.org/doi/abs/10.1145/3383313.3412264

(Best Short Paper)

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

ADER:自适应地蒸馏出Replay范例，以实现基于会话推荐的持续学习

https://dl.acm.org/doi/abs/10.1145/3383313.3412218

Long Papers 完整论文列表

Yoshifumi Seki, Takanori Maehara:

A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets. 4-12

Hanze Li, Scott Sanner, Kai Luo, Ga Wu:

A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems. 13-22

Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, Cong Geng:

Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison. 23-32

Chang Li, Haoyun Feng, Maarten de Rijke:

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity. 33-42

Yin Zhang, Ziwei Zhu, Yun He, James Caverlee:

Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. 43-52

Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas:

Contextual and Sequential User Embeddings for Large-Scale Music Recommendation. 53-62

Xu He, Bo An, Yanghua Li, Haikai Chen, Qingyu Guo, Xin Li, Zhirong Wang:

Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation. 63-72

Tobias Schnabel, Paul N. Bennett:

Debiasing Item-to-Item Recommendations With Small Annotated Datasets. 73-81

Guy Aridor, Duarte Gonçalves, Shan Sikdar:

Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems. 82-91

Yuta Saito:

Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions. 92-100

Mesut Kaya, Derek G. Bridge, Nava Tintarev:

Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance. 101-110

Gustavo Penha, Rodrygo L. T. Santos:

Exploiting Performance Estimates for Augmenting Recommendation Ensembles. 111-119

Liu Yang, Bo Liu, Leyu Lin, Feng Xia, Kai Chen, Qiang Yang:

Exploring Clustering of Bandits for Online Recommendation System. 120-129

Jing Lin, Weike Pan, Zhong Ming:

FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation. 130-139

Pigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Xiquan Cui, Edo Liberty, Khalifeh Al Jadda:

From the lab to production: A case study of session-based recommendations in the home-improvement domain. 140-149

Huazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan, Hongning Wang:

Global and Local Differential Privacy for Collaborative Bandits. 150-159

Samarth Aggarwal, Rohin Garg, Abhilasha Sancheti, Bhanu Prakash Reddy Guda, Iftikhar Ahamath Burhanuddin:

Goal-driven Command Recommendations for Analysts. 160-169

James Neve, Ryan McConville:

ImRec: Learning Reciprocal Preferences Using Images. 170-179

Jesús Omar Álvarez Márquez, Jürgen Ziegler:

In-Store Augmented Reality-Enabled Product Comparison and Recommendation. 180-189

Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof:

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. 190-199

Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, Xing Xie:

KRED: Knowledge-Aware Document Representation for News Recommendations. 200-209

Xu He, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang:

Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication. 210-219

Darius Afchar, Romain Hennequin:

Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction. 220-229

Ahmed Rashed, Shayan Jawed, Lars Schmidt-Thieme, Andre Hintsches:

MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems. 230-239

Steffen Rendle, Walid Krichene, Li Zhang, John R. Anderson:

Neural Collaborative Filtering vs. Matrix Factorization Revisited. 240-248

Mawulolo K. Ameko, Miranda L. Beltzer, Lihua Cai, Mehdi Boukhechba, Bethany A. Teachman, Laura E. Barnes:

Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation. 249-258

Rocío Cañamares, Pablo Castells:

On Target Item Sampling in Offline Recommender System Evaluation. 259-268

Hongyan Tang, Junning Liu, Ming Zhao, Xudong Gong:

Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. 269-278

Pan Li, Maofei Que, Zhichao Jiang, Yao Hu, Alexander Tuzhilin:

PURS: Personalized Unexpected Recommender System for Improving User Satisfaction. 279-288

Marialena Kyriakidi, Georgia Koutrika, Yannis E. Ioannidis:

Recommendations as Graph Explorations. 289-298

Panagiotis Symeonidis, Andrea Janes, Dmitry Chaltsev, Philip Giuliani, Daniel Morandini, Andreas Unterhuber, Ludovik Coba, Markus Zanker:

Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de. 299-308

Théo Moins, Daniel Aloise, Simon J. Blanchard:

RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues. 309-317

Jiaxi Tang, Hongyi Wen, Ke Wang:

Revisiting Adversarially Learned Injection Attacks Against Recommender Systems. 318-327

Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:

SSE-PT: Sequential Recommendation Via Personalized Transformer. 328-337

Jin Peng Zhou, Zhaoyue Cheng, Felipe Perez, Maksims Volkovs:

TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations. 338-347

Sami Khenissi, Mariem Boujelbene, Olfa Nasraoui:

Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System. 348-357

Gourab K. Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly:

Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World. 358-367

Bo-Wen Yuan, Yaxu Liu, Jui-Yang Hsia, Zhenhua Dong, Chih-Jen Lin:

Unbiased Ad Click Prediction for Position-aware Advertising Systems. 368-377

Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma:

Unbiased Learning for the Causal Effect of Recommendation. 378-387

Gustavo Penha, Claudia Hauff:

What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation. 388-397

Tobias Schnabel, Gonzalo A. Ramos, Saleema Amershi:

"Who doesn't like dinosaurs?" Finding and Eliciting Richer Preferences for Recommendation. 398-407

Short Papers 完整论文列表

Fei Mi, Xiaoyu Lin, Boi Faltings:

ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation. 408-413

Yagmur Gizem Cinar, Jean-Michel Renders:

Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation. 414-419

Walid Bendada, Guillaume Salha, Théo Bontempelli:

Carousel Personalization in Music Streaming Apps with Contextual Bandits. 420-425

Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei:

Causal Inference for Recommender Systems. 426-431

Denis Kotkov, Qian Zhao, Kati Launis, Mats Neovius:

ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering. 432-437

Francisco J. Peña, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, Aonghus Lawlor:

Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation. 438-443

Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Anderson Soares, Renan M. Oliveira, Sandor Caetano:

Contextual Meta-Bandit for Recommender Systems Selection. 444-449

Konstantina Christakopoulou, Madeleine Traverse, Trevor Potter, Emma Marriott, Daniel Li, Chris Haulk, Ed H. Chi, Minmin Chen:

Deconfounding User Satisfaction Estimation from Response Rate Bias. 450-455

Dalin Guo, Sofia Ira Ktena, Pranay Kumar Myana, Ferenc Huszar, Wenzhe Shi, Alykhan Tejani, Michael Kneier, Sourav Das:

Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. 456-461

Kosetsu Tsukuda, Masataka Goto:

Explainable Recommendation for Repeat Consumption. 462-467

Oren Barkan, Yonatan Fuchs, Avi Caciularu, Noam Koenigstein:

Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering. 468-473

Andres Ferraro, Dietmar Jannach, Xavier Serra:

Exploring Longitudinal Effects of Session-based Recommendations. 474-479

Jakim Berndsen, Barry Smyth, Aonghus Lawlor:

Fit to Run: Personalised Recommendations for Marathon Training. 480-485

Dmitri Goldenberg, Javier Albert, Lucas Bernardi, Pablo Estevez:

Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints. 486-491

Baptiste Barreau, Laurent Carlier:

History-Augmented Collaborative Filtering for Financial Recommendations. 492-497

Chu-Jen Shao, Hao-Ming Fu, Pu-Jen Cheng:

Improving One-class Recommendation with Multi-tasking on Various Preference Intensities. 498-502

Andrés Villa, Vladimir Araujo, Francisca Cattan, Denis Parra:

Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games. 503-508

Siyi Liu, Yujia Zheng:

Long-tail Session-based Recommendation. 509-514

Sung Min Cho, Eunhyeok Park, Sungjoo Yoo:

MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation. 515-520

Caojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, Wenzhe Shi:

Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. 521-526

Leyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov, Ivan V. Oseledets, Alexander Tuzhilin:

Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks. 527-532

Alessandro B. Melchiorre, Eva Zangerle, Markus Schedl:

Personality Bias of Music Recommendation Algorithms. 533-538

Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:

Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners. 539-544

Janhavi Dahihande, Akshay Jaiswal, Akshay Anil Pagar, Ajinkya Thakare, Magdalini Eirinaki, Iraklis Varlamis:

Reducing energy waste in households through real-time recommendations. 545-550

Ziwei Zhu, Yun He, Yin Zhang, James Caverlee:

Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. 551-556

Tushar Shandhilya, Nisheeth Srivastava:

Using conceptual incongruity as a basis for making recommendations. 557-561

[1] 

[2] 完整列表：https://dblp.org/db/conf/recsys/recsys2020.html