本期主要整理和分类了Recsys 2021的Research Papers和Reproducibility papers。按照推荐系统的研究方向和使用的推荐技术来分类，方便大家快速检索自己感兴趣的文章。个人认为Recsys这个会议重点不在于"技术味多浓"或者"技术多先进"，而在于经常会涌现很多新的观点以及有意思的研究点，涵盖推荐系统的各个方面，例如，Recsys 2021涵盖的一些很有意思的研究点包括：

• 推荐系统的信息茧房和回音室问题的探讨，有4篇文章探讨了社交媒体推荐、音乐推荐和视频推荐中的信息茧房和回音室效应。很少见到在学术会议上专门讨论这样深刻的问题，值得一读。

• 推荐系统评估体系的探讨，对推荐系统整个评估体系的梳理，多个指标间如何做权衡等。

• 推荐系统的交互设计探讨，探讨了美食推荐场景下用户交互设计。关于用户界面/交互设计的推荐系统文章还是很新奇的。

• 推荐系统中的探索与利用探讨，例如Google关于用户探索的工作Values of User Exploration in Recommender Systems值得一读。

• 对已有工作的探讨和挑战，传统矩阵分解推荐系统和深度学习推荐系统的对比。例如：何向南老师的NCF工作和MF的对比，继Recsys20被进行对比后, 在Recsys21上又再次被摆上了台面进行对比。

  • Recsys20, Rendle S, Krichene W, Zhang L, et al. Neural collaborative filtering vs. matrix factorization revisited[C]//Fourteenth ACM Conference on Recommender Systems. 2020: 240-248.

  • Recsys21, Anelli V W, Bellogín A, Di Noia T, et al. Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization[C]//Fifteenth ACM Conference on Recommender Systems. 2021: 521-529.

还有些研究点也是值得一读的，比如推荐系统中的冷启动，偏差与纠偏，序列推荐，可解释性，隐私保护等，这些研究很有意思和启发性，有助于开拓大家的研究思路。

下面主要根据自己读题目或者摘要时的一些判断做的归类，按照推荐系统研究方向分类、推荐技术分类以及专门实验性质的可复现型文章分类，可能存在漏归和错归的情况，请大家多多指正。

1.按照推荐系统研究方向分类

1.1 信息茧房和回音室

信息茧房/回音室(echo chamber)/过滤气泡(filter bubble)，这3个概念类似，在国内外有不同的说法。大致是指使用社交媒体以及带有算法推荐功能的资讯类APP，可能会导致我们只看得到自己感兴趣的、认同的内容，进而让大家都活在自己的小世界里，彼此之间难以认同和沟通。关于这部分的概念可参见知乎文章：https://zhuanlan.zhihu.com/p/71844281。有四篇文章探讨了这样的问题。

• The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending

  Tim Donkers and Jürgen Ziegler

• I want to break free! Recommending friends from outside the echo chamber

  Antonela Tommasel, Juan Manuel Rodriguez, and Daniela Godoy

• Follow the guides: disentangling human and algorithmic curation in online music consumption

  Quentin Villermet, Jérémie Poiroux, Manuel Moussallam, Thomas Louail, and Camille Roth

• An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes

  Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, and Maria Bielikova

1.2 探索与利用

此次大会在探索与利用上也有很多探讨，例如多臂老虎机、谷歌的新工作，即：用户侧的探索等。

• Burst-induced Multi-Armed Bandit for Learning Recommendation

  Rodrigo Alves, Antoine Ledent, and Marius Kloft

• Values of User Exploration in Recommender Systems

  Google, Minmin Chen, Yuyan Wang, Can Xu, Ya Le, mohit sharma, Lee Richardson, and Ed Chi

• Designing Online Advertisements via Bandit and Reinforcement Learning

  Yusuke Narita, Shota Yasui, and Kohei Yata

• The role of preference consistency, defaults and musical expertise in users’ exploration behavior in a genre exploration recommender

  Yu Liang and Martijn C. Willemsen

• Top-K Contextual Bandits with Equity of Exposure

  Olivier Jeunen and Bart Goethals

1.3 偏差与纠偏

涉及排序学习的纠偏、用户的偏差探索等。

Debiased Explainable Pairwise Ranking from Implicit Feedback

Khalil Damak, Sami Khenissi, and Olfa Nasraoui

Mitigating Confounding Bias in Recommendation via Information Bottleneck

Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming

User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms

Ningxia Wang, and Li Chen

1.4 冷启动

利用图学习、表征学习等做冷启动。

Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

Guillaume Salha-Galvan, Romain Hennequin, Benjamin Chapus, Viet-Anh Tran, and Michalis Vazirgiannis

Shared Neural Item Representations for Completely Cold Start Problem

Ramin Raziperchikolaei, Guannan Liang, and Young-joo Chung

1.5 评估体系

涉及离线或在线评估方法，准确性和多样性等统一指标的设计等。

Evaluating Off-Policy Evaluation: Sensitivity and Robustness

Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Diego Antognini and Boi Faltings

Online Evaluation Methods for the Causal Effect of Recommendations

Masahiro Sato

Towards Unified Metrics for Accuracy and Diversity for Recommender Systems

Javier Parapar and Filip Radlinski

1.6 会话/序列推荐

涉及session维度的短序列推荐；使用NLP中常用的Transformers做序列推荐的鸿沟探讨和解决，这个工作本人还挺感兴趣的，后续会精读下！

• Next-item Recommendations in Short Sessions

  Wenzhuo Song, Shoujin Wang, Yan Wang, and SHENGSHENG WANG

• Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation

  Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge

• Denoising User-aware Memory Network for Recommendation

  Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, kaikui liu, and Xiaolong Li

• Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning

  Xin Zhou and Yang Li

1.7 隐私保护

结合联邦学习做隐私保护等。

• Privacy Preserving Collaborative Filtering by Distributed Mediation

  Alon Ben Horin, and Tamir Tassa

• Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback

  Lorenzo Minto, Moritz Haller, Ben Livshits, and Hamed Haddadi

1.8 对抗与攻击

Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction

Zhenrui Yue, Zhankui He, Huimin Zeng, and Julian McAuley

1.9 对话推荐系统

Large-scale Interactive Conversational Recommendation System

Ali Montazeralghaem, James Allan, and Philip S. Thomas

1.10 可解释性推荐

EX3: Explainable Attribute-aware Item-set Recommendations

Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, S. Muthukrishnan, and Yongfeng Zhang

1.11 跨域推荐

Towards Source-Aligned Variational Models for Cross-Domain Recommendation

Aghiles Salah, Thanh Binh Tran, and Hady Lauw

1.12 基于视觉的推荐

利用视觉信息做推荐。

• Semi-Supervised Visual Representation Learning for Fashion Compatibility

Ambareesh Revanur, Vijay Kumar, and Deepthi Sharma

• Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor Network

Huiyuan Chen, Yusan Lin, Fei Wang, and Hao Yang

1.13 组推荐/用户物品分层推荐

• Local Factor Models for Large-Scale Inductive Recommendation

  Longqi Yang, Tobias Schnabel, Paul N. Bennett, and Susan Dumais

• Learning to Represent Human Motives for Goal-directed Web Browsing

  Jyun-Yu Jiang, Chia-Jung Lee, Longqi Yang, Bahareh Sarrafzadeh, Brent Hecht, Jaime Teevan

1.14 推荐系统交互设计

探讨了美食场景下，多用户意图的推荐系统的交互设计。

“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface

Alain Starke, Edis Asotic, and Christoph Trattner

2. 按照推荐技术分类

涉及传统协同过滤、度量学习的迭代；新兴的图学习技术、联邦学习技术、强化学习技术等的探索。

2.1 协同过滤

探索了传统的协同过滤工作，其中第一篇工作把CF和LDA联系在了一起，挺有意思。

Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All

Florian Wilhelm

Negative Interactions for Improved Collaborative-Filtering: Don’t go Deeper, go Higher

Harald Steck and Dawen Liang

ProtoCF: Prototypical Collaborative Filtering for Few-shot Item Recommendation

Aravind Sankar, Junting Wang, Adit Krishnan, and Hari Sundaram

2.2 图学习

知识图谱的应用以及图嵌入技术和上下文感知的表征技术的融合，这两个工作个人都挺感兴趣。

• Sparse Feature Factorization for Recommender Systems with Knowledge Graphs

 Antonio Ferrara, Vito Walter Anelli, Tommaso Di Noia, and Alberto Carlo            Maria Mancino

• Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations

 Marco Polignano, Cataldo Musto, Marco de Gemmis, Pasquale Lops, and       Giovanni Semeraro

2.3 强化学习

强化学习在推荐系统中的应用，和对话系统结合在一起；奖励函数的设计等。

• Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation

  Yaxiong Wu, Craig Macdonald, and Iadh Ounis,

• Pessimistic Reward Models for Off-Policy Learning in Recommendation

Olivier Jeunen and Bart Goethals

2.4 度量学习

协同过滤和度量学习的结合，即：CML。

• Hierarchical Latent Relation Modeling for Collaborative Metric Learning

  Viet-Anh Tran, Guillaume Salha-Galvan, Romain Hennequin, and Manuel Moussallam

2.5 联邦学习

联邦学习的优化以及在隐私保护中的应用。

• A Payload Optimization Method for Federated Recommender Systems

  Farwa K. Khan, Adrian Flanagan, Kuan Eeik Tan, Zareen Alamgir, and Muhammad Ammad-ud-din

• Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback

     Lorenzo Minto, Moritz Haller, Ben Livshits, and Hamed Haddadi

2.6 架构/训练/优化

涉及训练、优化、检索、实时流等。

• cDLRM: Look Ahead Caching for Scalable Training of Recommendation Models

  Keshav Balasubramanian, Abdulla Alshabanah, Joshua D Choe, and Murali Annavaram

• Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?

  Daichi Amagata and Takahiro Hara

• Page-level Optimization of e-Commerce Item RecommendationsChieh Lo, Hongliang Yu, Xin Yin, Krutika Shetty, Changchen He, Kathy Hu, Justin M Platz, Adam Ilardi, and Sriganesh Madhvanath

• Accordion: A Trainable Simulator for Long-Term Interactive Systems

  James McInerney, Ehtsham Elahi, Justin Basilico, Yves Raimond, and Tony Jebara

• Information Interactions in Outcome Prediction: Quantification and Interpretation using Stochastic Block Models

  Gaël Poux-Médard, Julien Velcin, and Sabine Loudcher

• Learning An Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems

  Danni Peng, Sinno Jialin Pan, Jie Zhang, and Anxiang Zeng

• Recommendation on Live-Streaming Platforms: Dynamic Availability and Repeat Consumption

Jeremie Rappaz, Julian McAuley, and Karl Aberer

3. 实验性质的文章

Reproducibility papers可复现实验性质的文章，共3篇。分别探索了：序列推荐中的采样评估策略；对话推荐系统中生成式和检索式的方法对比；神经网络推荐系统和矩阵分解推荐系统的对比。

• A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models

  by Alexander Dallmann, Daniel Zoller, Andreas Hotho (Data Science Chair, University of Würzburg, Würzburg, Germany)

• Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric Comparison

  by Ahtsham Manzoor and Dietmar Jannach (University of Klagenfurt, Klagenfurt, Austria)

• Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization

  by Vito Walter Anelli (Polytechnic University of Bari, Bari, Italy), Alejandro Bellogin (Information Retrieval Group, Universidad Autonoma de Madrid, Madrid, Spain), Tommaso Di Noia Polytechnic (University of Bari, Bari, Italy), and Claudio Pomo (Polytechnic University of Bari, Bari, Italy)

总结

通过此次的论文的整理和分类，笔者也发现了一些自己感兴趣的研究点，比如：推荐系统的回音室效应探讨文章；Transformers在序列推荐和NLP序列表征中的鸿沟和解决文章：Transformers4Rec；图嵌入表征和上下文感知表征的融合文章；NCF和MF的实验对比文章；谷歌的用户探索文章等。希望读者也能够发现自己感兴趣的文章。下期分享见！