WWW2020推荐系统论文合集(已分类整理)

国际顶级学术会议WWW2020定在2020年4月20-24日于中国台湾举办。受COVID-19疫情影响(疫情赶紧过去吧)，大会将在线上举行。今天是大会开始的第一天。

本次会议共收到了1129篇论文投稿，录用217篇，录取率仅为19.2%。其中关于推荐系统的论文大约38篇，推荐系统占比17.5%，可见推荐系统的研究受到学术界的广泛关注。另外，值得注意的是，接收的推荐系统论文中大部分都是与工业界合作的产物，因此不管是学术界还是工业界，推荐系统都是研究的热点与重点。

针对这38篇论文，我们进行了梳理分类，如下表所示

可见，推荐系统应用的文章以及序列化推荐的文章占比较大；随后是提升推荐效率、社会化推荐、常规推荐以及利用强化学习推荐；其次是兴趣点推荐、冷启动问题研究、推荐系统中的安全性、推荐公平性以及可解释推荐的文章；最后是各有一篇跨域推荐、利用知识图推荐、对话推荐系统以及用于点击率预估的推荐。

2 论文列表

1

Practical RS

• Graph Enhanced Representation Learning for News Recommendation
• Weakly Supervised Attention for Hashtag Recommendation using Graph Data
• Personalized Employee Training Course Recommendation with Career Development Awareness
• Understanding User Behavior For Document Recommendation
• Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
• paper2repo: GitHub Repository Recommendation for Academic Papers

2

Sequential RS

• Adaptive Hierarchical Translation-based Sequential Recommendation
• Attentive Sequential Model of Latent Intent for Next Item Recommendation
• Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
• Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation
• Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
• Keywords Generation Improves E-Commerce Session-based Recommendation

3

Efficient RS

• Learning to Hash with Graph Neural Networks for Recommender Systems
• LightRec: a Memory and Search-Efficient Recommender System
• A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems
• Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation

4

Social RS

• Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems
• The Structure of Social Influence in Recommender Networks
• Few-Shot Learning for New User Recommendation in Location-based Social Networks

5

Explainability for RS

• Directional and Explainable Serendipity Recommendation
• Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation

6

POI RS

• Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices
• A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data

7

General RS

• Efficient Neural Interaction Function Search for Collaborative Filtering
• Learning the Structure of Auto-Encoding Recommenders
• Deep Global and Local Generative Model for Recommendation

8

Fairness in RS

• Hierarchical Visual-aware Minimax Ranking Based on Co-purchase Data for Personalized Recommendation
• FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

9

RL for RS

• Off-policy Learning in Two-stage Recommender Systems
• Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation

10

Cross-domain RS

• Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation

11

Knowledge Graph RS

• Reinforced Negative Sampling over Knowledge Graph for Recommendation

12

Conversational RS

• Latent Linear Critiquing for Conversational Recommender Systems

13

CTR for RS

• Adversarial Multimodal Representation Learning for Click-Through Rate Prediction

3 官方Tutorial

最后，WWW2020还进行了两场关于推荐与搜索的Tutorial，分别是利用深度迁移学习的搜索与推荐和可信任的推荐与搜索系统，感兴趣的小伙伴可以学习一下。