SIGIR2020推荐系统论文聚焦

Posted 2021-05-01 小小挖掘机

目录

• 前言
• 推荐论文列表

前言

第43届国际信息检索研究和发展大会（SIGIR）将于2020年7月25-30日在美丽的中国西安举行。此次大会共收到了555篇长文投稿，录用147篇，长文录取率26.4%；共收到了507篇短文投稿，录用153篇，短文录取率30%。

正因为推荐与搜索是解决信息过载的两种有效途径，因此虽然是关于检索的会议，但通过下图可以看出推荐（Recommendation）占据了很大比例，与搜索（Search+Retrieval）不相上下。另外，图与网络（Graph/Network）数据成为研究的主要对象，毕竟许多待研究的对象都可以表示为图。值得注意的是，神经网络（Neural）仍然排在前列；融合知识（Knowledge）的搜索/推荐系统也被许多研究者研究。除此之外，强化学习也出现在了排行榜中，可见利用强化学习的思想来迭代优化搜索/推荐逐渐成为流行。

推荐论文列表

本次只对大会的长文（Full Papers）进行梳理，因此共整理出63篇关于推荐系统的论文。为了方便查看与了解，我们主要将其分为了以下几类：Sequential RS，Graph-based RS，Cold-start in RS，Efficient RS，Knowledge-aware RS，Robust RS，Group RS，Conversational RS，RL for RS，Cross-domain RS，Explainable RS，POI RS。另外，对于有一些不包含在以上类别的文章，我们统一归为了Others。当然，以上分类仁者见仁，智者见智，目的是给大家一个相对清晰的结构。具体的各个类别所包含的论文数见下表。

分类	数量
Sequential RS	8
Graph-based RS	6
Robust RS	6
Efficient RS	5
Knowledge-aware RS	5
Cold-start in RS	4
Group RS	4
Conversational RS	4
RL for RS	3
Cross-domain RS	2
Explainable RS	2
POI RS	1
Others	13

可见，序列化推荐的文章占比较大；随后是基于图的推荐、鲁棒的推荐系统；其次是提升推荐效率的文章、基于知识的推荐以及解决冷启动问题的推荐文章、组推荐、对话推荐系统；最后是强化学习推荐、跨域推荐、可解释推荐以及兴趣点推荐。当然其他类别中也包含了许多有意思的研究，比如消除推荐偏置（Bias）的文章、分布式训练推荐系统的文章以及如何retrain推荐系统的文章等。

接下来是分类好的推荐论文列表，大家可以根据自己的研究子方向进行精读。

Sequential RS

• Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation.

• GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation.

• Sequential Recommendation with Self-attentive Multi-adversarial Network.

• A General Network Compression Framework for Sequential Recommender Systems.

• Next-item Recommendation with Sequential Hypergraphs.

• KERL: A Knowledge-Guided Reinforcement Learning Model for  Sequential Recommendation.

• Time Matters: Sequential Recommendation with Complex Temporal Information.

• Modeling Personalized Item Frequency Information for Next-basket Recommendation.

Graph-based RS

• Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation.

• Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach.

• Multi-behavior Recommendation with Graph Convolution Networks.

• Hierarchical Fashion Graph Network for Personalised Outfit Recommendation.

• Neighbor Interaction Aware Graph Convolution Networks for Recommendation.

• Disentangled Representations for Graph-based Collaborative Filtering.

Cold-start RS

• Content-aware Neural Hashing for Cold-start Recommendation.

• Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste.

• Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation.

• AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems.

Efficient RS

• Lightening Graph Convolution Network for Recommendation.

• A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data.

• Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation.

• Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.

• Online Collective Matrix Factorization Hashing for Large-Scale Cross-Media Retrieval.

Knowledge-aware RS

• Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation.

• Fairness-Aware Explainable Recommendation over Knowledge Graphs.

• Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View.

• Make It a CHORUS: Context- and Knowledge-aware Item Modeling for Recommendation.

• CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems.

Robust RS

• How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models.

• GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Identification.

• How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models.

• Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines.

• DPLCF: Differentially Private Local Collaborative Filtering.

• Data Poisoning Attacks against Differentially Private Recommender Systems.

Group RS

• GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation.

• GroupIM: A Mutual Information Maximizing Framework for Neural Group Recommendation.

• Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation.

• Global Context Enhanced Graph Nerual Networks for Session-based Recommendation.

Conversational RS

• Deep Critiquing for VAE-based Recommender Systems.

• Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning.

• Towards Question-based Recommender Systems.

• Neural Interactive Collaborative Filtering.

RL for RS

• Self-Supervised Reinforcement Learning for Recommender Systems.

• MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations.

• Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs.

Cross-domain RS

• Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation.

• CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network.

Explainable RS

• Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations.

• Try This Instead: Personalized and Interpretable Substitute Recommendation.

POI RS

• HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation.

Others

• Learning Personalized Risk Preferences for Recommendation.

• Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates.

• Spatial Object Recommendation with Hints: When Spatial Granularity Matters.

• Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation.

• Distributed Equivalent Substitution Training for Large-Scale Recommender Systems.

• The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation.

• MVIN: Learning multiview items for recommendation.

• How to Retrain a Recommender System?

• Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems.

• BiANE: Bipartite Attributed Network Embedding.

• ASiNE: Adversarial Signed Network Embedding.

• Learning Dynamic Node Representations with Graph Neural Networks.

• Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback.